Generative AI Engineer

About the Role

Were seeking an engineer whos eager to design, build, and deploy next-generation Agentic AI systems. Youll translate cutting-edge LLM research into real-world applicationsdeveloping autonomous workflows, RAG pipelines, and model-driven services that operate reliably at scale. This position blends applied ML engineering with modern AI development frameworks and offers deep hands-on exposure across the GenAI lifecycle.

Responsibilities

• AI Application Development

  Design and orchestrate intelligent systems using modern generative and agentic AI frameworks; implement retrieval-augmented and fine-tuned model pipelines.

• Model Engineering

  Integrate and customize large language models; design structured prompts, evaluation logic, and lightweight tuning workflows to enhance contextual accuracy, speed, and cost.

• API & Service Deployment

  Develop scalable REST / FastAPI services, containerize them for cloud deployment, and apply MLOps best practices for versioning and monitoring.

• Data & Vector Pipelines

  Develop embedding pipelines, manage vector databases, and collaborate with data engineers for preprocessing and retrieval optimization.

Skills & Experience

• Programming & ML Foundations:

  Proficiency in Python with knowledge of data handling, ML pipelines, and deep-learning concepts.

• Generative AI Frameworks:

  Experience with LangChain / LangGraph / CrewAI / DSPy, or similar agentic toolkits.

• Vector Databases & Retrieval:

  Practical use of Milvus / Pinecone / ChromaDB / FAISS, or Azure AI Search for contextual search and embeddings.

• Cloud & Deployment:

  Exposure to Azure OpenAI, AWS Bedrock, or Vertex AI; containerization with Docker / Kubernetes; CI/CD using GitHub or Azure DevOps.

• MLOps & Monitoring:

  Familiar with MLFlow / Arize Phoenix / Weights & Biases, or equivalent tools for model tracking and observability.

• Prompt & Model Evaluation:

  Ability to design and test prompt templates using structured evaluation frameworks like AgentEval / DeepEval for quantitative and qualitative assessment.

Nice to Have

• Bachelors degree in Computer Science, AI/ML, or related field.
• Experience building LLM-based assistants or retrieval systems.
• Familiarity with Cursor, Claude Code or AI-assisted development environments.
• Contributions to open-source AI/ML projects or applied research.
• Understanding of Responsible AI, data privacy, and model governance.