Machine Learning Engineer with GEN AI Experience

Please find the position details below: Job Title: Machine Learning Engineer with GEN AI experience Location: Durham, NC or Boston MA, Merrimack NH & Smithfield RI ( 2 weeks remote , 2 weeks onsite) Duration: Long term Contract with possibility of Conversion Interview: 2 rounds, 1st – 60 mins Technical Panel, 2ND - 30 mins Manager call What the Client is Looking For: Core Technical Fit • Software Engineering Strength (10+ yrs) — APIs, microservices, cloud deployments. • Machine Learning Engineering (3–5 yrs) — experience building, deploying, and maintaining ML or GenAI solutions. • RAG Expertise — must have built and deployed Retrieval-Augmented Generation pipelines. • Vector Database Experience — FAISS, Pinecone, Weaviate, Milvus. • Agent Frameworks — LangChain, CrewAI, LangGraph, AutoGen. • Cloud Native Skills (AWS) — S3, Lambda, ECS, SageMaker. • DevOps — Docker, Kubernetes, GitHub Actions, CI/CD. • Observability — Prometheus, Grafana, OpenTelemetry (nice-to-have). • Data & Knowledge Graphs — Snowflake, Oracle, Neo4j, RDF/SPARQL. • AI Ethics & Governance — understanding of Responsible AI. What the Project Is About: This is a Machine Learning Engineering project within AI/ML division, focusing on integrating and productionizing AI/GenAI solutions. Team Context: • 15-member team: 6 are AI/ML specialists; 9 are on the BI (Business Intelligence) side. • This is the first AI delivery team in the organization — meaning they are defining AI/ML standards, pipelines, and best practices for future teams. Project Focus: The goal is to: • Deploy and scale AI/ML models (especially Generative AI and RAG-based solutions) into production. • Integrate agentic or multi-agent systems using frameworks like LangChain, CrewAI, LangGraph, or AutoGen. • Build cloud-native ML/GenAI pipelines on AWS (Lambda, ECS, S3, SageMaker). • Establish data retrieval and augmentation systems (RAG) using vector databases (FAISS, Pinecone, Weaviate, Milvus). • Develop monitoring, observability, and CI/CD practices for deployed ML models. • Promote Responsible AI practices across AI ecosystem. Essentially, this is a hands-on engineering role (not research-oriented) focused on: “Turning research and experimental models into scalable, production-grade AI systems.”