AI Enigneer
Hyderabad, IndiaHybridFull-timeMid · 4-7 years
About this role
Overview
We are hiring a Gen AI Engineer (Mid-level) to help design, build, and productionize generative AI features and systems. You’ll work within a cross-functional team of engineers, data scientists, and product managers to implement RAG pipelines, fine-tune models, and integrate agentic workflows—while following Responsible AI practices and scalable deployment patterns.
Key Responsibilities
- Architecture & Implementation: Design and implement components of multi-agent and pipeline-based GenAI systems using frameworks like LangChain, AutoGen, or Semantic Kernel under senior technical guidance.
- Model Engineering: Perform fine‑tuning and adaptation of LLMs/SLMs with methods such as LoRA/QLoRA, evaluate model performance, and iterate to improve domain accuracy.
- RAG & Retrieval: Build and optimize RAG pipelines—document chunking, embedding generation, vector DB integration (Pinecone/Chroma/Milvus), and hybrid search strategies.
- Integration & Ops: Collaborate to containerize services (Docker), assist in Kubernetes deployments, and contribute to CI/CD for model artifacts and inference services.
- Monitoring & Quality: Implement model and system monitoring (latency, accuracy, hallucination metrics), log pipelines, and support incident triage.
- Responsible AI: Apply guardrails to reduce bias, hallucinations, and data leakage; help operationalize privacy controls and compliance checks.
- Cross-functional Collaboration: Work with product, design, and business stakeholders to translate requirements into technical designs and estimate implementation effort.
- MCP Server Integration: Support and implement MCP Server integration tasks as required by product needs.
Required Technical Skills
- Core AI: Strong understanding of Transformer-based models and familiarity with diffusion or multi-modal model basics.
- Programming: Proficient in Python; experience with PyTorch or TensorFlow for model development.
- Agentic Frameworks: Hands-on experience building agent workflows or multi-step orchestration using LangChain, AutoGen, or similar.
- RAG & Vector DBs: Practical experience with document chunking, embedding models, and integrating vector databases (Pinecone, Chroma, Milvus); knowledge of hybrid search.
- Model Fine-tuning: Experience with LoRA/QLoRA or other parameter‑efficient tuning approaches.
- Cloud & Infra: Experience deploying services on cloud (AWS/Azure/GCP), containerization (Docker), and basic Kubernetes usage.
- Dev Practices: Familiarity with CI/CD pipelines for ML (model versioning, packaging), unit/integration testing for AI components.
- MCP Server integration knowledge required.
Experience & Qualifications
- Experience: 4–7 years in AI/ML or related software engineering roles, with at least 1+ years focused on Generative AI or LLM-driven projects.
- Collaboration: Experience working in cross-functional teams delivering production software.
- Communication: Ability to explain technical tradeoffs to technical and non-technical stakeholders.
Nice-to-Have
- Exposure to production-managed services such as AWS Bedrock, Azure AI Foundry, or Google Vertex AI.
- Familiarity with ML observability tools (e.g., Evidently, Weights & Biases) and model governance platforms.
- Contributions to open-source GenAI projects or relevant research/publications.
- Experience with multi-modal models (text + image/video/audio).
- Relevant certifications (AWS ML specialty, Azure AI Engineer).