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Converge Squad 2

AI Engineer – Senior Software Engineer

Converge Squad 2
Randolph, NJ $50k - $600k/yr Full time Posted 2h ago
Backend Developer Senior Artificial Intelligence Data Analysis Data Analytics Data Engineer Data Pipeline Data Platform Data Processing Data Science Databases Generative AI Information Technology (IT) Large Language Models Llm Operations Machine Learning Programming Programming Language Programming Languages Rag Architectures SQL
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Job Description

Converge Squad 2 is looking for a Senior Software Engineer to lead the design, build, and productionization of AI-powered solutions. This onsite role in Randolph, NJ blends practical software engineering with machine learning, generative AI, LLM capabilities, and cloud deployment. You will help deliver scalable, reliable, and secure AI applications from early prototyping through ongoing optimization.

Compensation: USD 50,000 - 600,000 per year
Experience: 5+ years
Employment Type: Full-time

What you will work on

  • Design and develop scalable AI-powered applications and services.
  • Build and integrate Generative AI and Large Language Model (LLM) capabilities into production applications.
  • Create AI/ML pipelines spanning data preparation, model training, evaluation, deployment, and monitoring.
  • Apply LLM patterns including embeddings, vector databases, Retrieval-Augmented Generation (RAG), prompt engineering, and AI agents.
  • Design and implement APIs and backend services that connect AI features to existing products and platforms.
  • Evaluate models and approaches across accuracy, latency, cost, scalability, and reliability.
  • Develop strategies for prompt optimization, model evaluation, hallucination reduction, and response quality.
  • Deliver production-ready systems with observability, including logging, monitoring, security, and failure handling.
  • Collaborate with product managers, software engineers, data scientists, and other stakeholders to translate business requirements into technical solutions.
  • Participate in architecture discussions, contribute to technical decisions, and conduct code reviews while helping establish engineering best practices.
  • Mentor other engineers and support the team’s technical growth.
  • Stay current with AI, machine learning, LLMs, and related technologies and identify where they can be applied to products.

Required qualifications

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
  • 5+ years of professional software engineering experience, including building and deploying production systems.
  • Strong programming skills in Python and/or Java.
  • Strong understanding of software engineering principles, object-oriented design, data structures, algorithms, and system design.
  • Hands-on experience with Generative AI, LLMs, or machine learning systems.
  • Experience working with APIs and integrating AI/ML models into applications.
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud.
  • Experience with databases and data processing systems.
  • Strong knowledge of testing, debugging, CI/CD, version control, and production operations.

Technologies you may use

Python, Java, SQL, Machine Learning, Generative AI, LLMs, NLP, embeddings, RAG, prompt engineering, model evaluation, LangChain, LlamaIndex, PyTorch, TensorFlow, REST APIs, microservices, distributed systems, AWS, Azure, Google Cloud, SQL and NoSQL databases, vector databases, Git, CI/CD, Docker, Kubernetes, monitoring and observability, OpenAI, Anthropic, Gemini, Pinecone, Weaviate, Milvus, OpenSearch, pgvector, Semantic Kernel, scikit-learn, MLOps/LLMOps

Preferred qualifications

  • Experience with foundation models such as OpenAI, Anthropic, Gemini, or others.
  • Experience building RAG-based applications using embeddings and vector databases.
  • Experience with vector databases including Pinecone, Weaviate, Milvus, OpenSearch, or pgvector.
  • Experience with frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
  • Experience developing AI agents and tool-using LLM applications.
  • Experience with ML frameworks including PyTorch, TensorFlow, or scikit-learn.
  • Experience with model evaluation, fine-tuning, or parameter-efficient fine-tuning.
  • Familiarity with MLOps/LLMOps practices.
  • Experience with Docker, Kubernetes, and cloud-native development.
  • Knowledge of AI security, data privacy, responsible AI, and model safety practices.
  • Experience designing highly scalable distributed systems.

Confidentiality

All information is kept confidential according to EEO guidelines.

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