Experience
3 - 8 yrs
Job Location
Bengaluru, India
Vacancy
1
Designation
AI Engineer
Job Type
ONSITE
Job Description
About the role
We are hiring an AI Engineer for our Labs team a fast-moving group that prototypes emerging AI capabilities and takes the most promising ones to production at scale. You will operate at the intersection of research and engineering: turning new papers and ideas into working demos within days, then hardening them into reliable, production-grade systems. The ideal candidate stays on the frontier of AI, is hands-on with the full ML lifecycle, and thrives in ambiguity with a strong bias for action.
Job Specific Duties and Responsibilities - End-to-end ML ownership: Drive the complete lifecycle data curation, model building, evaluation, deployment, monitoring, and retraining for both predictive and generative AI systems.
- Production-grade MLOps: Build scalable pipelines for training, CI/CD, model registry, A/B testing, drift detection, and automated retraining. Optimize inference for latency, throughput, and cost.
- LLMs and SLMs: Fine-tune and deploy open and closed models using techniques such as LoRA/QLoRA, PEFT, instruction tuning, and preference tuning (RLHF/DPO). Apply quantization and distillation where needed.
- Agentic systems: Design and productionize agentic frameworks RAG pipelines, tool/function calling, memory, planning loops, and multi-agent orchestration with appropriate guardrails and observability.
- Quality and trust: Build evaluation frameworks (offline + online, including LLM-as-judge and red-teaming). Diagnose and mitigate hallucinations, bias, and drift.
- Rapid innovation: Track SOTA research, prototype quickly, and showcase work through demos and tech talks to internal stakeholders and leadership.
- 3+ years of hands-on experience as an AI/ML Engineer or Applied Scientist, with proven production deployments including at least one LLM-based or agentic system taken to production.
- Strong Python skills and solid software engineering fundamentals (version control, testing, design patterns, code reviews).
- Deep Learning & NLP: Strong grasp of transformer architectures, attention, tokenization, embeddings, and modern NLP techniques.
- Hands-on with PyTorch and the Hugging Face ecosystem (Transformers, PEFT, TRL, Accelerate).
- Agentic & RAG stack: Working knowledge of frameworks such as LangChain / LangGraph / LlamaIndex / CrewAI / AutoGen, plus vector stores (Pinecone, Weaviate, Qdrant, pgvector, or FAISS) and reranking strategies.
- Serving & optimization: Experience with inference servers such as vLLM, TGI, or Triton, and familiarity with quantization (GPTQ, AWQ, GGUF).
- MLOps & infra: Hands-on with tools like MLflow, Weights & Biases, Airflow, or Kubeflow; comfortable with Docker, Kubernetes, GPU workloads, and at least one major cloud (AWS / Azure / GCP).
- Soft skills: High bias for action, strong communication, ownership mindset, and intellectual curiosity.
Nice to have:
- Open-source contributions, multimodal model experience, on-device SLM deployment, or familiarity with LLM security (OWASP LLM Top 10).
B.Tech or M.Tech in Computer Science, Data Science Engineering, AI/ML Engineering, or a closely related quantitative discipline. Equivalent practical experience supported by a strong portfolio (open-source work, publications, or production deployments) will also be considered.
SOFT SKILLS - Strong problem-solving and ownership mindset; comfortable operating in ambiguity.
- Clear communication of technical tradeoffs and experiment results to stakeholders.
- Collaborative approach with engineering, product, and data teams.