Job Title : Senior Analyst/Data Scientist- GenAI+DE
Location: Chennai, Bangalore, Hyderabad
Who we are
Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow.
Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence.
We are a Great Place to Work-Certified (2022-24), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the Best and Fastest Growing analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine
Curious about the role What your typical day would look like
We are seeking a Generative AI Engineer with strong supporting experience in Data Engineering to design and build enterprise-grade GenAI solutions. The ideal candidate will have hands-on experience working with LLMs, RAG pipelines, and GenAI frameworks, along with solid expertise in Python, PySpark, cloud platforms, and large-scale data pipelines.
This role focuses primarily on GenAI solution development, with Data Engineering skills used to enable high-quality data pipelines and model-ready datasets.
Key Responsibilities
- Design and develop Generative AI solutions using LLMs and modern GenAI frameworks
- Build RAG pipelines, prompt orchestration, and LLM-powered applications
- Integrate GenAI models with enterprise data platforms and APIs
- Develop and manage data ingestion, transformation, and modeling pipelines on cloud platforms
- Use Python and PySpark for large-scale data processing and AI workflows
- Prepare and optimize datasets for GenAI and ML use cases
- Work with vector databases and embedding pipelines
- Deploy and monitor GenAI applications in production environments
- Collaborate with data, ML, and platform teams for end-to-end delivery
Required Skills (Primary - GenAI)
- Hands-on experience with Generative AI / LLM applications
- Experience building RAG pipelines
- Experience with LangChain / LlamaIndex / similar frameworks
- Strong knowledge of prompt engineering and LLM workflows
- Experience integrating LLM APIs (OpenAI / Azure OpenAI / open-source models)
- Understanding of embeddings and vector search
Required Skills (Supporting - Data Engineering)
- Strong programming skills in Python
- Hands-on experience with PySpark / Spark
- Experience building data pipelines
- Experience in data ingestion, transformation, and data modeling
- Experience with at least one cloud platform (AWS / Azure / GCP)
- Experience processing structured and unstructured data
- Strong SQL skills
Good to Have
- Experience with vector databases (FAISS, Pinecone, Milvus, etc.)
- Experience with ML pipelines and model deployment
- Knowledge of orchestration tools (Airflow, ADF, Glue, etc.)
- Experience with MLOps / LLMOps practices
- Exposure to fine-tuning or evaluation frameworks
You are important to us, let's stay connected!
Every individual comes with a different set of skills and qualities so even if you don't tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow. We are an equal opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire.
Note: The designation will be commensurate with expertise and experience. Compensation packages are among the best in the industry.
Additional Benefits: Health insurance (self & family), virtual wellness platform, and knowledge communities
