Gen AI Engineer

We are looking for Gen AI Engineer for our client in Hanover, NJ

Job Title: Gen AI Engineer

Job Location: Hanover, NJ

Job Type: Contract

Job Overview:

Pay Range: $65hr - $70hr

Requirement/Must Have:

• Clear hands-on GenAI/AI/ML project experience, not just theoretical knowledge.
• Experience with LLM application development, RAG, embeddings, vector databases, prompt engineering, fine-tuning, evaluation, and deployment.
• Experience with agentic orchestration using frameworks like LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, or MCP-based workflows.
• Strong coding skills in Python.
• Experience with APIs and cloud exposure such as AWS/Azure/Google Cloud Platform.
• Practical experience with LLMOps, monitoring, testing, and production deployment.

Responsibilities:

• Validate strong coding skills in Python.
• Check whether the candidate has worked on real business use cases and handled end-to-end implementation.
• Explain architecture, model selection, data flow, latency, cost, and accuracy improvement.

Nice to Have:

• Experience with LLM APIs and models: OpenAI, Azure OpenAI, Anthropic, Gemini, Llama, Mistral, Bedrock.
• Fine-tuning or model customization experience.
• Understanding of evaluation techniques: accuracy, relevance, groundedness, hallucination testing, latency, cost, token usage.
• Cloud deployment experience: AWS, Azure, or Google Cloud Platform.
• Experience with databases and data pipelines: SQL, NoSQL, S3/Blob, Databricks, Snowflake, Spark.

Skills:

• Strong verbal and written communication skills.
• Excellent communication and presentation skills.
• Ability to communicate effectively with stakeholders.

Qualification And Education:

• Must have clear past hands-on project experience in AI/ML or GenAI, not only certifications or theory.
• Should have built real LLM applications, such as RAG, chatbots, document Q&A, copilots, summarization, classification, or workflow automation.
• Experience with agentic orchestration is highly preferred.
• Should clearly mention RAG implementation: embeddings, chunking, vector databases, retrieval strategy, reranking, hallucination reduction.
• Good signs include experience with Docker, FastAPI, APIs, CI/CD, Kubernetes, serverless, monitoring.
• Strong candidate can explain business problem, solution design, model choice, data flow, risks, and measurable impact.
• Prefer candidates who have worked with security, PII handling, access control, guardrails, and compliance.