Generative AI Engineer

We’re seeking an engineer who’s eager to design, build, and deploy next-generation Agentic AI systems. You’ll translate cutting-edge LLM research into real-world applications—developing autonomous workflows, RAG pipelines, and model-driven services that operate reliably s position blends applied ML engineering with modern AI development frameworks and offers deep hands-on exposure across the GenAI lifecycle.

• 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 Lang Chain / Lang Graph / CrewAI / DSPy, or similar agentic toolkits.
• Vector Databases & Retrieval: Practical use of Milvus / Pinecone / Chroma
  
  DB / 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 Git Hub or Azure Dev Ops.
• 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 Agent Eval / Deep Eval for quantitative and qualitative assessment.

Nice to Have

• Bachelor’s 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.