Software Engineer – AI & Data Science

About the job:
We’re hiring a Software Engineer – AI & Data Science to build and ship production AI systems across real client and internal use cases. You’ll work across data, models, and LLM-powered applications, from exploring a problem to measuring results to deploying the solution in production.
This is a full-time, work-from-office role in Hyderabad.
About us:
We’re a new age tech & business services company, HQ’ed in Hyderabad with clients across the world. Find out more at https://www.linkedin.com/company/gitforce
What You’ll Do
TL;DR: Turn data and AI models into production systems that solve real business problems.
Build AI-powered applications using LLM APIs (OpenAI, Anthropic, Google, and open-source models)
Build RAG pipelines, and tune retrieval quality using embeddings, chunking strategies, and reranking
Design and run evaluations for LLM and ML systems: define metrics, build test sets, and measure quality
Develop AI agents, tool-calling workflows, and multi-step LLM systems
Build and maintain data pipelines that prepare, clean, and transform data for AI and analytics use cases
Train, fine-tune, or adapt models where off-the-shelf approaches fall short
Analyze data and model outputs to find failure modes and drive improvements
Ship your work as production code, including Python services and APIs, working with the engineering team
Work closely with product, founders, and client teams to turn business problems into data and AI solutions
Qualifications
TL;DR: A strong data science foundation, solid Python engineering, and hands-on experience building AI-powered applications.
You Are:
2 to 5 years into your professional career in data science, ML, or AI engineering
A data scientist who writes production-quality code, not just notebooks
Someone who measures before claiming something works
Comfortable picking up new models, frameworks, and tools quickly
Someone who enjoys owning a problem end to end, from data to deployed system
Must-haves:
Strong Python, including pandas, NumPy, and writing clean, tested, production-ready code
Solid SQL and experience working with real-world, messy data
Strong grasp of ML fundamentals: statistics, model evaluation, and experimentation
Hands-on experience with LLM APIs, including prompt engineering, structured outputs, and tool calling
Experience building RAG pipelines or AI agents (LangChain, LlamaIndex, LangGraph, or similar)
Experience designing evaluations for ML or LLM systems
Strong problem-solving and communication skills
Nice-to-haves:
Fine-tuning LLMs or training ML models (PyTorch, Hugging Face, scikit-learn)
Vector databases: Pinecone, pgvector, Weaviate, Qdrant, Chroma, or similar
LLM observability and evaluation tools (Langfuse, LangSmith, Braintrust, or similar)
Building APIs with FastAPI or Flask
Data tooling: dbt, Airflow, Spark, or similar
Docker, and cloud (AWS, GCP, or Azure)

Work arrangement
No