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Ai Solutions Architecture Engineer Jobs

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Today

Staff AI Engineer/AI Solution Architect, Conversation Intelligence Systems

Xenoss New York, NY

New York, NY

Today

Full time Backend Developer Ai Solutions Architecture Analytics

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Job Description

Xenoss is hiring a Staff AI Engineer / AI Solution Architect to lead the applied AI architecture for a long-term In-Call Assistant initiative for a world-leading financial services organization. The work focuses on designing real-time conversational intelligence that detects customer needs, objections, and buying signals, then recommends the required process steps with compliance guardrails.

In this PoC-first engagement, you will set the technical direction for signal and intent understanding, model training and evaluation, and the move from offline experimentation to production-grade low-latency inference, all executed inside the client perimeter.

Role context and project scope

Own the applied AI architecture and evaluation strategy for a complex enterprise AI program at Staff/Architect level.
Lead the AI architecture for a long-term In-Call Assistant initiative that supports front-office employees during live customer conversations.
Design a system that identifies customer needs, objections, buying signals, and required process steps, delivering concise, context-aware recommendations.
Cover end-to-end system design including low-latency signal detection, context preparation, specialist recommendation generation, RAG over approved product and policy knowledge, confidence management, and compliance guardrails.
Help define how the AI architecture, models, evaluation framework, and feedback loops evolve from an initial offline version to live production use.

Responsibilities

Lead the applied AI architecture across the In-Call Assistant lifecycle, from data and taxonomy design to model training, evaluation, and production readiness.
Design the end-to-end AI architecture for the In-Call Assistant.
Define signal and trigger taxonomies for live conversations.
Design training strategies for signal detection and specialist recommendation models.
Shape data preparation, annotation, and SME validation workflows.
Evaluate fine-tuning, post-training, RAG, and hybrid approaches.
Design low-latency signal detection, routing, context preparation, and confidence management.
Design evaluation frameworks, golden datasets, and model improvement cycles.
Define grounding, guardrails, abstention, and policy-compliance behavior.
Make trade-offs between model quality, latency, cost, explainability, and governance.
Partner with AI engineers, data engineering, MLOps, and client SMEs.
Define the AI approach for the conversation intelligence PoC, including event/intent/insight taxonomy and golden dataset strategy and annotation workflow.
Establish evaluation frameworks and acceptance criteria, driving trade-offs between accuracy, explainability, latency, cost, and governance.
Decide which modeling approaches are appropriate for each use case.
Act as an escalation point for AI architecture, evaluation, and data strategy decisions.
Work within a cross-functional team spanning AI engineering, data engineering, MLOps, solution architecture, and client stakeholders.
Partner with domain SMEs on taxonomy, labeling, and validation.
Mentor engineers working on extraction, evaluation, and data pipelines.
Translate ambiguous business use cases into testable AI hypotheses and validation plans.

Requirements

Strong hands-on experience with applied AI/ML systems in production-oriented environments.
Experience with NLP, conversational AI, or transcript-based intelligence systems.
Ability to design evaluation frameworks, not just run experiments.
Experience building or validating structured datasets from unstructured text.
Strong understanding of LLM-based extraction, classification, RAG, and fine-tuning trade-offs.
Practical knowledge of classical ML or predictive modeling.
Understanding of probability-based prediction, calibration, and outcome evaluation.
Comfort working with messy enterprise data and incomplete labels.
Ability to communicate with both technical teams and business stakeholders.
Strong ownership of ambiguity, scope control, and PoC validation strategy.
Financial services domain exposure.
Experience with sales, call center, or customer conversation analytics.
Speech/ASR pipeline familiarity.
Model governance and auditability experience.
Experience with real-time AI systems or low-latency inference.
Experience combining unstructured conversation signals with structured CRM, transaction, or customer profile data.
Experience designing golden datasets and SME review workflows.

Technologies

LLM, SFT, DPO, preference optimization, LoRA, QLoRA, PEFT
PyTorch, Hugging Face
RAG, embeddings, retrieval, MLOps

Technology landscape

LLM and smaller-model training for conversational AI
SFT, DPO / preference optimization, LoRA / QLoRA, and PEFT
PyTorch and Hugging Face ecosystem
Signal extraction and multi-label classification
RAG and knowledge-grounded recommendation generation
Embeddings, retrieval, and context preparation
Low-latency model serving and inference optimization
Golden dataset creation, annotation, and SME validation
Model evaluation, confidence calibration, and error analysis
MLOps, monitoring, feedback loops, and model governance

Delivery and work environment

Engagement structure: FTE-equivalent via long-term B2B contract.
Work location: On-site or closely aligned with the client team in New York.
Infrastructure: Client environment only, with no external training or data processing environments.
Data residency: All work executed within the client perimeter.
Delivery mode: PoC-first, with a path toward production-grade conversation intelligence and prediction systems.

Location: New York, NY (onsite)

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1d ago

AI Engineer - FDE Software Engineering Sr. Manager

Accenture Overland Park, KS

Overland Park, KS

1d ago

$123k - $302k/yr Full time Manager Agentic Ai Agentic Ai Orchestration

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Job Description

Accenture is hiring an AI Engineer to embed with clients and deliver secure, operational agentic workflows in enterprise environments.

Responsibilities

Design and engineer enterprise-ready AI agents including retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability
Create abstraction layers across AI providers (Anthropic, Google, OpenAI, etc.) to support seamless integration and enablement
Build scalable AI-native systems using containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability
Tailor and deploy agentic applications across verticals such as finance, healthcare, and retail, supporting domain-specific processes with intelligent automation
Lead design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders and support trust and adoption
Define and apply key metrics, test harnesses, and evaluation plans to measure accuracy, latency, safety, and cost effectiveness
Develop reusable patterns, documentation, and best practices to influence internal assets and client roadmaps

Requirements

Minimum 10 years of experience with end-to-end software engineering and SDLC expertise
Minimum 8 years of experience with:
- Programming in Python, Java, or equivalent, plus familiarity with evaluation tooling, logging, monitoring, and agent observability
- Production deployment practices, including CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging
- Client communication and collaboration, including leading technical workshops and delivering under ambiguity
Minimum 3 years of experience with:
- Cloud-native systems (APIs, microservices, containerization, serverless)
- AI platforms (OpenAI, Claude, plus open-source models), including building abstraction layers for multi-provider pipelines
Minimum 2 years of hands-on experience designing and delivering Agentic AI solutions
Minimum 1.5 years of experience with:
- Semantic models, ontologies, knowledge graphs, or enterprise knowledge
- Coding agents and AI-assisted software development
Minimum 1 year of expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments
Bachelor's degree or equivalent (minimum 12 years work experience); if Associate Degree, must have minimum 6 years work experience

Technologies

Python, Java
Anthropic, Google, OpenAI, Claude
Open-source models
Kubernetes, Docker
Microservices, serverless, event-driven architectures
CI/CD, Terraform, Helm
Semantic models, ontologies, knowledge graphs
RAG
APIs

Benefits

Medical, dental, vision
Life and long-term disability coverage
401(k) plan
Bonus opportunities
Paid holidays
Paid time off

How You’ll Work

Embed directly with clients as both technologist and trusted advisor
Partner with stakeholders to define use cases, rapidly prototype, and deploy robust, secure, operational agentic workflows in complex enterprise domains
Support net-new platforms and systems, integrating solutions alongside client environments and Ecosystem partners

Bonus Points

Served as an Agentic AI Engineer in an Enterprise environment
Additional AI certifications or agentic tool experience
Defined or worked with enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry/stream-based architectures
Understanding of the AI-native paradigm blending cloud-native with generative model architectures, optimized for performance, modularity, and efficiency
Delivered solutions across multiple industries (e.g., finance, healthcare) by tailoring agentic workflows to industry needs

Location: Overland Park, KS (onsite)
Salary: USD 122,700 - 302,400 per yearly
Travel: Potential up to 100%

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Ai Solutions Architecture Engineer Jobs

Staff AI Engineer/AI Solution Architect, Conversation Intelligence Systems

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