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

REQUIRED QUALIFICATIONS

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, robust 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).

EDUCATION

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.

Transparent communication of technical tradeoffs and experiment results to stakeholders.

Collaborative approach with engineering, product, and data teams.

📌 AIML Engineer (Karnataka)
🏢 Aziro
📍 Karnataka