Evnek
Website:
evnek.com
Job details:
Job Title: Staff AI Engineer — Agentic AI & ML
Experience: 7-10yrs
Location: Coimbatore (Onsite – 5 Days/Week)
Notice Period: Immediate Joiners Only
About the Role
We are seeking a highly experienced Staff AI Engineer (Lead) to design, build, and scale next-generation agentic AI systems—autonomous solutions capable of reasoning, planning, and executing complex workflows.
In this role, you will lead the architecture and development of AI systems across LLMs, machine learning models, and production-grade infrastructure, while mentoring engineers and driving technical excellence.
Key Responsibilities
- Architect and orchestrate multi-agent AI systems for autonomous task execution
- Design and implement LLM pipelines using frameworks like LangChain, LangGraph, and CrewAI
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines
- Integrate external tools and services via APIs, databases, and microservices
- Define and manage agent memory, context handling, and reasoning flows
- Develop, train, and deploy ML models (classification, ranking, anomaly detection)
- Establish robust MLOps pipelines (experiment tracking, monitoring, versioning)
- Fine-tune and optimize models using techniques such as LoRA and quantization
- Lead technical design decisions and mentor a team of AI/ML engineers
Required Qualifications
- 7–10 years of experience in software engineering with 4+ years in AI/ML
- Strong hands-on experience with Large Language Models (LLMs), prompt engineering, and fine-tuning
- Proven expertise in building agentic/autonomous AI systems
- Proficiency in Python and modern AI frameworks (LangChain, AutoGen, CrewAI)
- Experience with vector databases and RAG architectures
- Solid understanding of APIs, distributed systems, and cloud platforms (AWS, GCP, Azure)
Technology Stack
- Languages: Python, TypeScript
- AI/ML: PyTorch, TensorFlow, Hugging Face
- LLMs: OpenAI, Anthropic, Gemini, LLaMA, Mistral
- Data & Vector DBs: Pinecone, Weaviate, Elasticsearch
- MLOps: MLflow, Kubeflow, Airflow
- Infrastructure: Docker, Kubernetes, Terraform, AWS/GCP
Click on Apply to know more.