We are looking for a skilled ML Engineer with 3 to 6 years of experience in building and deploying production-grade AI solutions, particularly around LLMs, RAG systems, and agentic AI frameworks. The role involves designing end-to-end ML architectures, optimizing models at scale, and delivering client-ready AI solutions. You will collaborate closely with stakeholders, mentor junior engineers, and drive AI projects from experimentation to production.

What will you need to be successful in this role?

Core Technical Skills :

- Strong hands-on experience with Python for ML/AI (NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow)

- Proven experience deploying production LLM applications with 1M+ tokens processed

- Advanced prompt engineering expertise including ReAct, meta-prompting, and function calling

- Production experience with RAG systems including hybrid search and re-ranking

- Deep understanding of embedding models and vector databases at scale

- Experience with agentic AI frameworks (LangGraph, CrewAI, or AutoGen)

- Strong knowledge of LLM evaluation frameworks (RAGAS, LLM-as-judge patterns)

- Experience implementing multi-agent systems and orchestration

- Proficiency with cloud ML platforms (AWS SageMaker, Azure ML, or Vertex AI)

Advanced Capabilities :

- Experience with model fine-tuning (LoRA, QLoRA, PEFT, instruction tuning)

- Knowledge of knowledge graphs and graph-based RAG implementations

- Understanding of model hosting, inference optimization, and cost management

- Experience with MLOps pipelines, CI/CD for ML, and model versioning

- Ability to architect end-to-end ML solutions from data ingestion to deployment

- Experience with data pipelines and ETL for ML workflows

- Proficiency in containerization and orchestration (Docker, Kubernetes)

Client Engagement & Delivery :

- Experience presenting technical solutions to clients and stakeholders

- Ability to translate business requirements into technical ML solutions

- Track record of delivering client POCs and production implementations

- Experience creating technical documentation and implementation guides

Good to have :

- Experience hosting private LLMs (7B-13B models on-premises or cloud)

- Knowledge of graph databases (Neo4j) and graph neural networks

- Experience with streaming and real-time ML inference

- Published research papers or contributions to open-source ML projects

- DeepLearning.AI certifications in Agentic AI, RAG, or Finetuning

- AWS/Azure ML certifications or working towards them

Competencies :

- Excellent verbal and written communication skills

- Strong mentoring ability for junior ML engineers

- Self-driven with ability to work independently on complex problems

- Excellent problem-solving skills with systematic debugging approach

- Proactive ownership of projects from ideation to deployment

- Ability to stay current with rapidly evolving AI/ML landscape

- Excellent academic record B.E./B.Tech, MCA,

The job is for:

May work from home