Design, develop, and deploy machine learning models and pipelines using various frameworks and tools. Collaborate with cross-functional teams to integrate AI solutions into cloud-native software engineering. Stay updated with the latest advancements in machine learning and integrate them into projects.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
Job Title: Machine Learning Engineer - W2 only - We can provide sponsorship as well
Duration: Long Term
Location: Durham, NC/Boston, MA/Merrimack/NH/Smithfield, RI - Hybrid
Manager's top asks:
Bachelor’s or Master’s in Computer Science, Artificial Intelligence, Machine Learning, or related field.
-10+ years of software engineering experience in APIs, cloud deployments, and system integration.
-3-5 years in ML engineering, with 2+ years in agentic or multi-agent systems.
-Proven must have experience building and deploying RAG pipelines using embedding models and vector search.
-Must have hands-on experience with vector databases such as FAISS, Pinecone, Weaviate, or Milvus.
-Must have experience with agent orchestration frameworks (LangChain, CrewAI, LangGraph, AutoGen etc ).
-Strong background in cloud-native software engineering and microservices architecture.
-Concrete understanding of traditional ML models and their usecases.
-Programming:
Advanced Python skills;
familiarity with C++,
Java, or .NET is a plus.
-Cloud Platforms: Proficiency with AWS services (S3, Lambda, ECS, SageMaker, etc.).
-Databases: Experience with Oracle, Snowflake, vector databases, and knowledge graphs (e.g., Neo4j, RDF/SPARQL).
-DevOps: CI/CD pipelines, Docker, Kubernetes, GitHub Actions.
-AI Ethics: Understanding of Responsible AI principles
The Expertise We’re Looking For
- Bachelor’s or Master’s in Computer Science, Artificial Intelligence, Machine Learning, or related field.
- 10+ years of software engineering experience in APIs, cloud deployments, and system integration.
- 3-5 years in ML engineering, with 2+ years in agentic or multi-agent systems.
- Proven must have experience building and deploying RAG pipelines using embedding models and vector search.
- Must have hands-on experience with vector databases such as FAISS, Pinecone, Weaviate, or Milvus.
- Must have experience with agent orchestration frameworks (LangChain, CrewAI, LangGraph, AutoGen etc ).
- Strong background in cloud-native software engineering and microservices architecture.
- Concrete understanding of traditional ML models and their usecses.
- Programming: Advanced Python skills; familiarity with C++, Java, or .NET is a plus.
- Cloud Platforms: Proficiency with AWS services (S3, Lambda, ECS, SageMaker, etc.).
- Databases: Experience with Oracle, Snowflake, vector databases, and knowledge graphs (e.g., Neo4j, RDF/SPARQL).
- DevOps: CI/CD pipelines, Docker, Kubernetes, GitHub Actions.
- AI Ethics: Understanding of Responsible AI principles and ability to identify and mitigate ethical risks.
- Good to have if you have exposure or worked on tools which aid for continuous model evaluation and alerting.
- Stay updated with the latest advancements in Machine Learning world and integrate them into projects.
- Communicate complex technical concepts to non-technical stakeholders.