Learn. Thrive.
Make a Difference.

Experience: 4–5 Years

We are seeking a Senior GenAI Engineer who can design, build, and deploy production-grade Generative AI solutions. This role combines hands-on GenAI development (LLMs, RAG, prompt engineering) with practical ML engineering and deployment skills.

This is not a pure research role and not a pure DevOps role. The ideal candidate is comfortable taking an AI use case from idea → prototype → production, working closely with product and engineering teams.

Key Responsibilities: Generative AI & Modeling

• Design and implement LLM-powered applications using models such as GPT, LLaMA, Claude, or similar
• Build Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases
• Perform prompt engineering, prompt optimization, and evaluation
• Fine-tune LLMs using PEFT / LoRA techniques where required
• Evaluate model outputs for quality, hallucination, latency, and cost

Deployment & ML Engineering

• Deploy models as REST APIs using frameworks such as FastAPI or Flask
• Containerize AI services using Docker
• Build and maintain ML pipelines for training, inference, and monitoring
• Implement basic model monitoring and drift detection
• Optimize inference performance pragmatically (latency, throughput, cost)

Collaboration & Ownership

• Work closely with product managers to translate business problems into AI solutions
• Collaborate with backend and platform teams for integration and deployment
• Take ownership of AI components in production, including reliability and maintainability
• Stay current with emerging trends in GenAI and applied AI systems

Required Skills & Qualifications

• 4–6 years of experience in Machine Learning / Data Science / GenAI
• Strong proficiency in Python
• Hands-on experience with PyTorch or TensorFlow
• Practical experience working with LLMs and GenAI systems
• Experience building RAG pipelines using vector databases such as FAISS, Pinecone, Weaviate, or ChromaDB
• Solid understanding of prompt engineering and LLM evaluation
• Experience deploying models using REST APIs
• Working knowledge of Docker
• Strong problem-solving and analytical skills

Cloud & Infrastructure (Working Knowledge)

• Experience deploying AI models on cloud platforms (Azure preferred)
• Familiarity with Kubernetes (deployment and scaling basics)
• Understanding of ML lifecycle concepts (training, inference, monitoring)

Good-to-Have Skills

• Experience with LangChain, LlamaIndex, or Hugging Face
• Exposure to on-prem or GPU-based deployments
• Experience with MLOps tools (MLflow, Airflow, Kubeflow, DVC)
• Knowledge of model optimization techniques (quantization, batching)
• Familiarity with CI/CD pipelines for ML systems

Ideal Candidate Profile

• T-shaped engineer: deep in GenAI + broad in ML engineering
• Comfortable working in ambiguity and early-stage problem spaces
• Bias toward practical, production-ready solutions over academic perfection
• Strong communicator who can explain AI trade-offs to non-technical stakeholders

Education

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related field (or equivalent practical experience)