- Company Name
- NeerInfo Solutions
- Job Title
- Data Scientist
- Job Description
-
**Job Title**
Data Scientist / Gen AI Lead Consultant
**Role Summary**
Lead the design, development, and deployment of Generative AI and Agentic AI solutions across multiple industries. Drive end‑to‑end implementation of large language model (LLM) pipelines, Retrieval‑Augmented Generation (RAG), and AI‑enabled data products. Collaborate with on‑site and offshore teams, ensure production‑grade delivery, and mentor junior engineers.
**Expectations**
- Minimum 7 years of IT experience with at least 4 years focused on GenAI/Agentic AI and data science.
- Hands‑on expertise in Python and at least one additional language (R, Scala, Java, SQL).
- Ability to travel as required and work closely with distributed/offshore teams.
- Strong communication skills to translate technical concepts to stakeholders.
**Key Responsibilities**
- Architect and implement GenAI applications using frameworks such as LangGraph, AutoGen, or Crew AI.
- Deploy LLMs and advanced RAG solutions on cloud platforms (AWS, GCP, Azure, IBM Watson).
- Ingest and process unstructured data (PDF, HTML, images, audio‑to‑text) and build vector‑search pipelines with FAISS, Pinecone, Weaviate, or Azure AI Search.
- Develop, fine‑tune, and evaluate LLMs; manage model lifecycle with tools like DeepEval, FMeval, RAGAS, or Bedrock.
- Build RESTful APIs (FastAPI, Flask, Django) and integrate AI services into production systems.
- Implement CI/CD pipelines and DevOps practices using Jenkins, GitHub Actions, Terraform, etc.
- Apply deep‑learning techniques (CNN, RNN, LSTM) and stay current with research trends.
- Conduct data quality, statistical analysis, and visualization (Tableau, SQL/Hive) to support model validation.
- Lead Agile/Lean development cycles, mentor junior staff, and ensure best‑practice coding standards.
**Required Skills**
- Python (expert), plus any of: R, Scala, Java, SQL.
- Generative AI frameworks: LangGraph, AutoGen, Crew AI.
- Cloud AI services: AWS, GCP, Azure, IBM Watson.
- Vector databases: FAISS, Pinecone, Weaviate, Azure AI Search.
- ML/DL libraries: TensorFlow, PyTorch, LangChain.
- LLM fine‑tuning, local deployment of open‑source models.
- CI/CD & DevOps: Jenkins, GitHub Actions, Terraform.
- Databases: PostgreSQL, MongoDB, CosmosDB, DynamoDB, Hive, Spark.
- Big Data tools: HDFS, Hive, Spark, Scala (preferred).
- API development: FastAPI, Flask, Django.
- Model evaluation: DeepEval, FMeval, RAGAS, Bedrock.
- Data visualization: Tableau; statistical analysis (regression, hypothesis testing).
**Required Education & Certifications**
- Bachelor’s degree in Computer Science, Data Science, Engineering, or related field (or foreign equivalent).
- Minimum 3 years progressive experience may substitute for each year of formal education.
- Relevant certifications (e.g., AWS Certified Machine Learning, GCP Professional Data Engineer, Azure AI Engineer) are a plus but not mandatory.