AI Engineer

Aziro; Chennai District, Bengaluru, Pune District; Hybrid; INR 1,200,000 - 1,800,000; Full time

Job summary

Aziro in Bengaluru is looking for an experienced AI/ML Engineer to take ownership of end-to-end machine learning processes. You will drive activities from data curation to production deployment, focusing on predictive and generative AI systems.

The ideal candidate should have at least 4 years of experience, strong Python skills, and knowledge of deep learning and NLP techniques. This position offers an engaging work environment where innovation and collaboration thrive.

Qualifications

• 4+ years of experience as an AI/ML Engineer with production deployments.
• Strong skills in Python and software engineering fundamentals.
• Hands-on experience with transformers and modern NLP techniques.

Responsibilities

• Drive the complete lifecycle of data and model management.
• Build production-grade MLOps pipelines for scalability.
• Design and productionize agentic frameworks and pipelines.

Skills

Deep Learning

Natural Language Processing (NLP)

Python

MLOps

Docker

Kubernetes

Education

B.Tech or M.Tech in Computer Science or related field

Tools

PyTorch

Hugging Face

MLflow

Weights & Biases

Job description

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

• 4+ 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 re‑ranking 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).

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.


• Clear communication of technical tradeoffs and experiment results to stakeholders.


• Collaborative approach with engineering, product, and data teams.