Autogen

Autogen Engineer Jobs

4 jobs found

2d ago

Senior AI Engineer

Diverse Lynx Aguadilla

Aguadilla

2d ago

$50 - $55/hr Contract Senior Agentic Ai AI

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Job Description

Diverse Lynx is seeking a Senior AI Engineer to lead the design, development, and deployment of agentic AI capabilities, with a focus on autonomous multi-agent systems. The role partners with business, data, and platform teams to deliver scalable, secure, production-grade AI platforms. This onsite contract position in Aguadilla, Puerto Rico offers an hourly rate of $50 to $55.

Responsibilities

Architect and implement Agentic AI systems, incorporating planning, reasoning, task execution, memory, and feedback loops.
Develop multi-agent architectures including task agents, reasoning agents, tool-calling agents, and orchestration agents.
Apply retrieval-augmented generation, Knowledge Graph, and memory-based AI patterns for enterprise use cases.
Integrate large language models with enterprise systems, APIs, tools, and workflows.
Define end-to-end AI solution architecture from data ingestion to inference and observability.
Build reusable AI services, agent frameworks, and orchestration layers.
Ensure AI solutions are scalable, resilient, performant, and secure.
Collaborate with cloud, data, and MLOps teams to productionize solutions.
Define agent evaluation metrics, including accuracy, autonomy, cost, latency, and safety.
Embed Responsible AI guardrails covering governance, explainability, auditability, and compliance.
Work with product owners, domain SMEs, and architects to translate business problems into AI solutions.
Mentor junior engineers and contribute to AI engineering standards and best practices.
Support client discussions, solution walkthroughs, and technical decision making.

Requirements

Strong proficiency in Python (mandatory).
Hands-on experience with Agentic AI frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI, or equivalents.
Experience with large language models including OpenAI, Azure OpenAI, Anthropic, and open-source models.
Expertise in RAG, embeddings, vector databases, and prompt engineering.
Experience building multi-agent orchestration, tool calling, and workflow automation.
Experience with vector stores such as Pinecone, FAISS, Weaviate, Chroma, and similar technologies.
Knowledge of knowledge graphs, ontologies, and semantic search.
Cloud experience on Azure, AWS, or GCP.
Familiarity with APIs, microservices, and event-driven architectures.
Model monitoring, evaluation, and cost optimization.
8–12+ years of overall software or AI engineering experience.
3+ years hands-on experience in Generative AI and emerging Agentic AI systems.
Experience delivering production-grade AI solutions in enterprise environments.
Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field.

Technologies

Python
LangGraph
LangChain
LlamaIndex
AutoGen
CrewAI
OpenAI
Azure OpenAI
Anthropic
Pinecone
FAISS
Weaviate
Chroma
Azure
AWS
GCP
APIs
Microservices
Event driven architectures
Knowledge Graph

Nice to Have

Experience in regulated industries such as pharma, healthcare, or life sciences.
Exposure to knowledge graphs, scientific/R&D workflows, or enterprise automation.

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3d ago

Forward Deployment AI Engineer

GadellNet St. Louis, MO

St. Louis, MO

3d ago

$120k - $150k/yr Full time AI Ai Workflows Artificial Intelligence

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Job Description

GadellNet places the Forward Deployment AI Engineer on site with client organizations in St. Louis, MO to design, build, and deploy production grade AI solutions that deliver measurable operational impact.

Responsibilities

Work directly with clients to uncover operational pain points, map workflows, and identify high ROI AI and automation opportunities.
Architect and implement AI agents using frameworks like LangChain, LangGraph, AutoGen, or equivalent, covering tool usage, memory handling, and multi-agent orchestration.
Create AI powered workflows that integrate with clients' existing systems such as CRMs, ERPs, cloud platforms, and communication tools.
Build automation solutions across document processing, data extraction, reporting, customer communications, and internal operations.
Manage the full delivery lifecycle from scoping and architecture through development, testing, deployment, and lightweight documentation.
Coach and enable client teams to maintain and iterate on solutions after deployment.
Act as a trusted technical advisor, translating AI capabilities into clear business value for non-technical stakeholders.
Contribute reusable components, playbooks, and delivery templates to scale the practice internally.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field
Must be legally authorized to work in the United States without employer sponsorship now or in the future
Minimum of 2 years of professional experience in software engineering, AI/ML engineering, or a technical consulting role
Proficiency in Python with familiarity in REST APIs and cloud services
Hands-on experience building with large language model APIs (OpenAI, Anthropic, Gemini, or similar)
Understanding of prompt engineering, retrieval augmented generation, and agentic workflow patterns
Ability to work independently in ambiguous, fast-moving client environments
Strong communication skills, able to explain complex concepts like vector databases to non-technical stakeholders

Technologies

Python
LangChain
LangGraph
AutoGen
OpenAI
Anthropic
Gemini
REST APIs
Cloud services
Zapier
Make
n8n
Pinecone
Weaviate
Chroma

Benefits

Health, vision, and dental insurance
Paid time off including parental leave and volunteer time
Flexible work environment
Access to mental health services
Employee matching donation program
401k and employer matching

Position Summary

As a Forward Deployment AI Engineer, you will embed with client teams to design, build, and deploy AI driven solutions that deliver real operational impact. You will work on-site with small and mid-size businesses to diagnose inefficiencies, earn stakeholder trust, and ship production grade AI agents and automated workflows that transform daily operations.

Values

Make an Impact
100% Responsibility 0% Excuses
Grow or Die

Eligibility to Work in the US

GadellNet does not offer work visa sponsorship for this role. All candidates must be legally authorized to work in the United States at the time of application. Employment is contingent on verification of identity and eligibility to work in the United States.

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5d ago

Senior AI Engineer

Flex Employee Services Irvine, CA

Irvine, CA

5d ago

$47 - $51/hr Full time Agentic Ai AI Ai Ml

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Job Description

The Senior AI Engineer role at Flex Employee Services focuses on designing, building, and scaling a production-grade Generative AI and Data Platform on AWS, with emphasis on LLM powered capabilities, vector search, graph-based knowledge systems, and governed data pipelines.

Responsibilities

Design and operationalize LLM powered applications using retrieval augmentation (RAG), embeddings, prompt orchestration, and evaluation frameworks.
Develop vector search solutions leveraging Amazon OpenSearch.
Build graph-based knowledge systems with Amazon Neptune.
Integrate AWS data stores such as ElastiCache (Redis) and DynamoDB to support AI workloads.
Construct agentic workflows using LangGraph, AutoGen, CrewAI, or equivalent frameworks.
Incorporate LangChain or LlamaIndex for retrieval orchestration, tool calling, and context management.
Define standards for tool integration and context sharing following MCP style designs.
Evaluate LLM models and retrieval strategies with a focus on latency, accuracy, cost, and context limits.
Design and build scalable data pipelines using Databricks and Apache Spark.
Develop data ingestion, transformation, document processing, embedding generation, and indexing pipelines.
Ensure data quality through validation, completeness, consistency, and ongoing monitoring.
Implement data governance, access controls, retention policies, auditability, and lineage tracking.
Develop secure and scalable backend services and APIs with robust design principles.
Define API standards, versioning, reliability, retry logic, circuit breakers, and idempotency.
Build reusable platform capabilities that serve multiple teams and applications.
Establish and manage CI/CD pipelines to support rapid development cycles.
Deploy production systems using Docker and Kubernetes, with blue/green, canary, rollback, and feature flag strategies.
Monitor platform reliability, observability, security, data freshness, and cost optimization.
Define and monitor GenAI quality metrics including grounding, retrieval relevance, response consistency, latency, and cost.
Implement prompt and version tracking, evaluation pipelines, and continuous improvement workflows.
Ensure AI security through access controls, authentication, data protection, responsible AI guardrails, privacy, and auditability.

Requirements

5+ years of experience in Generative AI and LLM work including RAG, embeddings, and prompt engineering.
Hands-on experience with AWS Cloud services such as OpenSearch, Neptune, DynamoDB, and ElastiCache/Redis.
Strong background in vector search and retrieval systems (OpenSearch or vector databases).
Proficiency with graph databases and knowledge graph concepts (Amazon Neptune).
Experience with LLM frameworks such as LangChain or LlamaIndex.
Experience with agentic AI frameworks like LangGraph, AutoGen, or CrewAI.
Experience building data pipelines with Databricks and Apache Spark (including embedding pipelines).
Backend/API development expertise in Python, including scalable APIs and microservices.
Proven track record of delivering production-grade Generative AI solutions.
Strong Python programming skills and familiarity with distributed systems, API design, and scalable backend development.
Experience building end-to-end AI/ML platforms and lifecycle delivery.
Education: Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field.

Technologies

LangChain, LlamaIndex
LangGraph, AutoGen, CrewAI
OpenSearch, Amazon Neptune, DynamoDB
ElastiCache (Redis)
Databricks, Apache Spark
Python, Docker, Kubernetes

Benefits

Dental insurance
Health insurance
Referral program
Vision insurance

Preferred Skills

Model evaluation frameworks and LLM observability tools
AI governance and compliance frameworks
Kubernetes and advanced MLOps practices
Model Context Protocol (MCP) patterns
Agent-based architectures

Domain Experience

AI/ML Platform Engineering
Generative AI / LLM Applications
Data Platform / Big Data Engineering

Soft Skills

Strong problem-solving and analytical thinking
Ability to communicate complex AI concepts clearly
Collaborative and cross-functional mindset
Ownership-driven and proactive execution

Application Questions

Are you comfortable working on W2, or should you wait for a future 1099/C2C opportunity?
Are you willing to work on a contract basis, or should you wait for future full-time opportunities?
Do you have at least 5 years of experience with Graph Databases (Amazon Neptune, Knowledge Graphs)?
Do you have at least 5 years of experience with Agentic AI Frameworks (LangGraph / AutoGen / CrewAI)?
Do you have at least 5 years of experience with Databricks and Apache Spark (data pipelines, embedding pipelines)?
Do you have at least 5 years of experience with Backend/API Development (Python, scalable APIs, microservices)?
Do you have at least 5 years of experience with Generative AI / LLM (RAG, embeddings, prompt engineering)?
Do you have at least 5 years of experience with AWS Cloud (OpenSearch, Neptune, DynamoDB, ElastiCache/Redis)?
Do you have at least 5 years of experience with Vector Search and Retrieval Systems (OpenSearch / Vector DB)?

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23d ago

Lead Principal Machine Learning Engineer

Oracle San Francisco, CA

San Francisco, CA

23d ago

Full time Senior Agent Orchestration AI

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Job Description

The Lead Principal Machine Learning Engineer at Oracle leads the design, development, and operation of next generation AI systems on Oracle Cloud Infrastructure. Based onsite in San Francisco, CA, this senior technical role requires a PhD and at least 12 years of relevant experience, directing architecture and engineering for enterprise AI platforms, autonomous workflows, scalable inference, and business‑critical AI applications deployed at scale.

Responsibilities

Assume senior technical ownership of OCI AI platform capabilities, including agent execution, inference services, model serving, AI workflow orchestration, evaluation, and observability.
Design and deliver scalable agentic AI systems capable of reasoning, planning, tool use, workflow execution, multi-step task orchestration, and safe human-in-the-loop escalation.
Develop production grade services for tool invocation, agent memory, context management, Model Context Protocol integration, vector retrieval, multi-agent coordination, policy enforcement, and evaluation.
Direct architecture across distributed services with a focus on low latency, high throughput, GPU efficiency, reliability, cost control, operability, and secure multi-tenant operation.
Define service boundaries, APIs, data models, state management, consistency tradeoffs, failure modes, SLIs/SLOs, rollout strategies, and readiness criteria for AI platform services.
Shape technical strategy across infrastructure, platform, security, data, and application teams, translating broad goals into executable multi-quarter plans with clear milestones.
Integrate AI agents securely and reliably with enterprise APIs, cloud services, databases, identity systems, secrets management, and external systems.
Establish AgentOps and LLMOps practices for tracing, monitoring, evaluation suites, regression testing, experimentation, safety guardrails, prompt/tool versioning, and production reliability.
Evaluate and operationalize emerging technologies in generative AI, agentic workflows, inference optimization, long-context systems, reasoning models, AI developer tooling, and agent-first development.
Promote engineering excellence through code and design reviews, test strategy, deployment automation, incident analysis, documentation, and AI-assisted development using tools such as Codex, Claude Code, Cursor, Copilot, or equivalents.
Mentor staff and senior engineers, elevate architectural standards, and influence OCI engineering practices without direct management authority.
Own critical production outcomes including reliability, performance, security posture, cost efficiency, and maintainability of delivered systems.

Requirements

PhD in Computer Science, AI/ML, Engineering, or a related field, or equivalent practical experience.
12+ years of professional software engineering experience with substantial ownership of production systems, or equivalent impact at senior staff or principal levels.
Proven track record as a Staff, Senior Staff, Principal, or equivalent technical leader influencing architecture and execution across multiple teams.
Deep experience designing, building, and operating high-scale distributed systems, cloud services, infrastructure platforms, or AI/ML platform services.
Hands-on experience with production AI systems, agentic AI applications, autonomous workflows, tool-using agents, multi-step orchestration, or multi-agent systems.
Practical experience with orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, LlamaIndex, or similar ecosystems.
Deep understanding of LLM application patterns, including prompt design, structured outputs, function/tool calling, context management, RAG, memory, tool safety, and evaluation.
Strong Python programming skills with the ability to contribute production code, reviews, tests, and debugging in complex distributed environments.
Extensive expertise with Kubernetes, Docker, cloud-native infrastructure, service-to-service communication, scalability, fault tolerance, observability, and performance analysis.
Experience defining SLIs/SLOs, production readiness criteria, incident response practices, monitoring, tracing, experiments, and reliability programs for AI or distributed systems.
Solid understanding of AI safety, governance, security, and operational risks for autonomous or semi-autonomous systems, including data handling, access control, auditability, and human accountability.
Excellent written and verbal communication, with demonstrated ability to lead technical direction, resolve ambiguity, and influence senior stakeholders.

Technologies

Python
Kubernetes
Docker
LangGraph
LangChain
CrewAI
AutoGen
LlamaIndex
Model Context Protocol (MCP)
Oracle Cloud Infrastructure (OCI)
Codex
Claude Code
Cursor
Copilot

Preferred Qualifications

Experience optimizing large-scale GPU inference or training workloads for latency, throughput, utilization, availability, and cost.
Experience building or operating model serving components, inference gateways, agent runtimes, workflow engines, developer platforms, or internal AI productivity platforms.
Experience integrating AI systems with enterprise APIs, databases, cloud services, vector databases, embeddings, retrieval systems, identity systems, and policy enforcement layers.
Experience with LLM fine-tuning, long-context systems, reasoning models, model routing, caching, batching, quantization, or emerging generative AI research.
Experience building evaluation frameworks for agentic systems, including offline evals, online experiments, golden tasks, adversarial testing, regression gates, and observability dashboards.
Experience using AI-assisted software development tools such as Codex, Claude Code, Cursor, Copilot, or similar systems in large-scale engineering environments.
Track record of defining architectural standards, platform capabilities, or engineering practices adopted across multiple teams or organizations.
Experience in enterprise, cloud infrastructure, regulated, security-sensitive, or mission-critical environments.

Location

San Francisco, CA (onsite)

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