Senior Engineer - Data Science/ML Engineer

Job Title:

Sr. Engineer (Data Scientist/ML Engineer)

Role Summary:

We are hiring a Sr. Engineer (2+ years) for the AI CoE to build and deploy Generative AI, RAG, Agentic AI, and Machine Learning solutions. This role combines LLM-driven applications with strong ML fundamentals, supporting scalable and monetizable AI products for telecom and enterprise use cases.

Key Responsibilities:

Build and deploy LLM-based applications, RAG, and Agentic RAG systems

Develop multi-agent workflows using tools like LangChain, LlamaIndex, LangGraph, Azure, and AWS Bedrock

Integrate MCP tools, APIs, and function-calling into AI systems

Design prompting strategies, embeddings, and vector search pipelines using Milvus / FAISS / Pinecone

Implement structured output generation for LLM responses (JSON/schema-driven outputs)

Apply machine learning techniques (classification, clustering, predictive modeling) for telecom and enterprise use cases

Perform data preprocessing, feature engineering, and model evaluation

Optimize solutions for performance, scalability, cost, and latency

Develop backend APIs using FastAPI for serving AI/ML models

Containerize applications using Docker for scalable deployment

Implement monitoring, logging, and observability using Grafana and ELK stack

Enable LLM traceability and observability using Langfuse etc.

Collaborate with product and engineering teams to deliver production-grade AI solutions

Required Skills:

2+ years of experience in Data Science / AI / Machine Learning /Gen AI

Excellent proficiency in Python and working knowledge of SQL (PostgreSQL preferred)

Hands-on experience with Machine Learning algorithms and model development

Strong understanding of ML concepts: supervised/unsupervised learning, feature engineering, model evaluation, bias-variance tradeoff

Hands-on experience with LLMs, Generative AI, RAG architectures, and Agentic workflows

Experience with LangChain / LlamaIndex / LangGraph / vector databases (Milvus, FAISS, Pinecone)

Experience building APIs using FastAPI

Understanding of structured output handling in LLMs

Solid foundation in statistics and data analysis

Good to Have:

Exposure to MCP tools and LLM orchestration frameworks

Experience with Agentic AI / multi-agent systems

Cloud experience (Azure / AWS / GCP)

Experience with Langfuse (LLM tracing & monitoring)

Familiarity with Grafana, ELK stack for monitoring

Experience with Docker-based deployments

Key Focus Areas:

Agent-based chatbots & virtual assistants

RAG-based knowledge systems

Multi-agent automation workflows (LangGraph-based)

ML-driven use cases (prediction, recommendation, analytics) in telecom

Scalable AI APIs and microservices using FastAPI

Additional Note:

Role involves hands-on Python coding and system design assessments, covering machine learning fundamentals, model building, RAG, LLMs, agent-based applications, LangGraph workflows, API development, and production deployment considerations (Docker, monitoring, traceability).