Senior Data Scientist / Generative AI Specialist

Position: Senior Data Scientist / Generative AI Specialist

Experience: 8+ Years

Employment Type: Full-Time

Level: Senior

Location: Remote

Position Overview

We are seeking a highly experienced Senior Data Scientist / Generative AI Specialist to design, develop, and productionize advanced AI/ML and Generative AI solutions. The ideal candidate will have strong hands-on expertise in Python, Machine Learning, Deep Learning, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, NLP, and MLOps .

The role requires the ability to translate complex business problems into scalable AI solutions, build enterprise-grade GenAI applications, develop and optimize machine-learning models, and establish reliable evaluation, deployment, monitoring, and governance practices.

Key Responsibilities

- Design, develop, and deploy enterprise-grade Data Science, Machine Learning, Generative AI, and Agentic AI solutions .
- Build and productionize LLM-powered applications using models from OpenAI, Anthropic Claude, Google Gemini, Llama, and other foundation-model ecosystems.
- Architect and implement Retrieval-Augmented Generation (RAG) solutions integrating enterprise structured and unstructured data.
- Develop advanced AI agents and multi-agent workflows using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or equivalent technologies.
- Implement tool/function calling, structured outputs, workflow orchestration, memory/context management, and agent reasoning patterns for enterprise AI applications.
- Design embedding, semantic-search, hybrid-search, metadata-filtering, and vector retrieval pipelines using vector databases such as Pinecone, FAISS, ChromaDB, Weaviate, Milvus, Elasticsearch/OpenSearch, or equivalent platforms.
- Develop multi-stage retrieval and reranking strategies to improve RAG accuracy, relevance, grounding, and contextual quality.
- Apply advanced prompt engineering and context engineering techniques, including prompt templates, few-shot learning, structured prompting, and dynamic context construction.
- Establish comprehensive LLM evaluation frameworks covering relevance, groundedness, faithfulness, hallucination, retrieval quality, latency, cost, and response quality.
- Implement LLMOps/MLOps capabilities including experiment tracking, prompt/model versioning, automated evaluation, regression testing, deployment, observability, and production monitoring.
- Develop predictive, classification, clustering, recommendation, forecasting, anomaly-detection, and optimization models using appropriate statistical and machine-learning techniques.
- Perform feature engineering, exploratory data analysis, statistical modeling, model selection, hyperparameter optimization, and model validation .
- Build scalable data-processing and feature pipelines using Python, SQL,



Pandas, NumPy, Spark/PySpark , and related technologies.
- Apply Deep Learning and NLP techniques using PyTorch, TensorFlow, Hugging Face Transformers, BERT-family models, and modern transformer architectures.
- Fine-tune or adapt foundation models using techniques such as PEFT, LoRA/QLoRA, instruction tuning, and domain-specific adaptation where appropriate.
- Design APIs and reusable AI services using FastAPI, REST APIs, microservices, and event-driven architectures .
- Containerize and orchestrate AI workloads using Docker and Kubernetes and deploy solutions across AWS, Azure, and/or Google Cloud.
- Implement CI/CD and automated model-deployment pipelines using technologies such as GitHub Actions, Jenkins, Azure DevOps, Terraform, MLflow, Kubeflow, and Airflow .
- Optimize AI applications for accuracy, latency, throughput, scalability, reliability, token consumption, and inference cost .
- Implement responsible AI practices covering AI security, PII protection, guardrails, prompt-injection defenses, content safety, access controls, auditability, and model governance .
- Collaborate with product managers, architects, data engineers, software engineers, security teams, and business stakeholders to translate requirements into production AI capabilities.
- Lead technical design discussions, architecture reviews, code reviews, experimentation, and proof-of-concept initiatives.
- Mentor junior data scientists and AI/ML engineers while defining engineering standards and reusable AI development patterns.
- Evaluate emerging GenAI, Agentic AI, LLM, multimodal, retrieval, and AI infrastructure technologies and recommend appropriate solutions for enterprise adoption.

Required Qualifications
- 8+ years of professional experience in Data Science, Machine Learning, AI, software/data engineering, or related disciplines.
- Solid recent hands-on experience developing Generative AI and LLM-based applications .
- Advanced programming expertise in Python and strong proficiency in SQL .
- Strong understanding of Machine Learning, Deep Learning, NLP, statistics, probability, optimization, and experimental design .
- Hands-on experience with LLMs, RAG architectures, embeddings, vector databases, semantic search, and prompt engineering .
- Experience developing Agentic AI applications , including tool-enabled agents and multi-step/multi-agent workflows.
- Experience with frameworks such as LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex ,



or comparable frameworks.
- Experience with PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers, Pandas, and NumPy .
- Strong knowledge of LLM evaluation, RAG evaluation, hallucination mitigation, retrieval optimization, model monitoring, and observability .
- Experience deploying production AI/ML solutions on AWS, Azure, and/or Google Cloud Platform .
- Experience with Docker, Kubernetes, REST APIs, FastAPI, Git, CI/CD, and cloud-native architectures .
- Understanding of enterprise MLOps/LLMOps, model lifecycle management, security, governance, and responsible AI .

Preferred Qualifications
- Experience with GraphRAG, knowledge graphs, multimodal AI, Text-to-SQL, MCP/tool integration, and advanced agent orchestration .
- Experience with managed AI platforms such as Azure AI Foundry/Azure OpenAI, AWS Bedrock/SageMaker, or Google Vertex AI .
- Experience with MLflow, Kubeflow, Airflow, Databricks, Snowflake, Spark, or similar enterprise data/ML platforms .
- Experience optimizing LLM inference, caching, batching, model routing, and AI application costs.
- Experience designing reusable enterprise AI platforms, AI gateways, agent frameworks, or GenAI shared services .
- Experience working with highly regulated or data-sensitive enterprise environments is preferred.

Technical Skills Programming: Python, SQL, Java/Scala (plus)

Data Science: Pandas, NumPy, Scikit-learn, SciPy, Statistical Modeling, Feature Engineering

ML/DL: PyTorch, TensorFlow, Keras, Hugging Face Transformers

Generative AI: LLMs, RAG, Agentic AI, Prompt Engineering, Context Engineering, Function/Tool Calling, Structured Outputs

LLM Frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen

Models: OpenAI GPT, Anthropic Claude, Google Gemini, Llama and other open-source/foundation models

Vector/Search: FAISS, Pinecone, ChromaDB, Weaviate, Milvus, Elasticsearch/OpenSearch

MLOps/LLMOps: MLflow, Kubeflow, Airflow, Model/Prompt Versioning, Evaluation, Monitoring, Observability

Cloud: AWS, Azure, GCP, Bedrock, SageMaker, Azure OpenAI/AI Foundry, Vertex AI

Deployment: Docker, Kubernetes, FastAPI, REST APIs, Microservices, CI/CD, Terraform

Data: Spark/PySpark, Databricks, Snowflake, Relational/NoSQL Databases

Education

Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering , or a closely related technical discipline.

Ideal Candidate The ideal candidate combines the analytical depth of a Senior Data Scientist with the production engineering capabilities of a Generative AI/LLM Engineer . This individual should be capable of taking an AI initiative from problem formulation and experimentation through architecture, implementation, evaluation, production deployment, monitoring, and continuous optimization .

📌 Senior Data Scientist --US shift Remote (India)
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