Senior Data Scientist / ML Engineer

Photon; Bangalore Rural, Bengaluru, Chennai District; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Photon seeks a Senior Data Scientist / ML Engineer specializing in Generative AI to design, evaluate, and productionize AI/ML solutions for enterprise apps, including RAG systems and AI agents. The role emphasizes improving retrieval quality, reducing hallucinations, and benchmarking LLMs for reliable, governed GenAI deployments.

You will lead efforts to build AI evaluation frameworks, optimize prompts and retrieval workflows, and collaborate with AI engineering to ship production-grade models

Qualifications

• 5–10 years of experience in data science, machine learning, or related applied ML roles.
• Strong Python programming skills with Pandas, NumPy, and Scikit-learn.
• Foundation in supervised/unsupervised learning, statistical modeling, feature engineering, and model evaluation.
• Hands-on Generative AI experience with LLM evaluation, prompt engineering, RAG architectures, embedding models, fine-tuning, and agent evaluation.
• Experience with PyTorch and/or TensorFlow.
• Exposure to OpenAI models, Claude, Gemini, and/or open-source LLMs.
• Experience with vector databases, semantic search, and retrieval optimization.
• Experience delivering production AI/ML solutions in enterprise environments.
• Experience working with distributed onshore/offshore teams.

Responsibilities

• Design and develop machine learning and Generative AI solutions.
• Build and optimize RAG pipelines, retrieval strategies, embeddings, and semantic search.
• Evaluate and benchmark LLMs for accuracy, performance, and reliability.
• Develop AI evaluation frameworks for hallucination detection, accuracy, bias/toxicity, and ground-truth validation.
• Optimize prompts, models, and retrieval workflows.
• Collaborate with AI engineering teams to deploy models into production.
• Create training, validation, and testing datasets.
• Perform model benchmarking, A/B testing, and performance analysis.
• Fine-tune foundation models when required.
• Implement model monitoring, observability, and ongoing evaluation processes.

Skills

Python

Pandas

NumPy

Scikit-learn

PyTorch

TensorFlow

LLM evaluation

RAG architectures

Embedding models

Prompt engineering

Model evaluation

Prompt optimization

Distributed teams

Tools

Databricks

MLflow

Spark

Neo4j

Azure AI Foundry

AWS Bedrock

Kubernetes

Job description

Primary Objective

We are seeking a Senior Data Scientist / ML Engineer specializing in Generative AI to design, evaluate, optimize, and productionize AI/ML solutions for enterprise applications—including RAG systems, AI agents, intelligent automation, and model evaluation platforms.

The role focuses on improving AI accuracy and retrieval quality, reducing hallucinations, benchmarking LLMs, and building reliable solutions for enterprise-scale deployments.

Success looks like:

measurable gains in retrieval/answer quality, robust evaluation frameworks in production, and clear collaboration with AI engineering to ship governed, reliable GenAI systems.

Key Responsibilities

Primary

• Design and develop machine learning and Generative AI solutions.
• Build and optimize RAG pipelines, retrieval strategies, embeddings, and semantic search.
• Evaluate and benchmark LLMs for accuracy, performance, and reliability.
• Develop AI evaluation frameworks for hallucination detection, accuracy measurement, bias/toxicity detection, and ground-truth validation.
• Optimize prompts, models, and retrieval workflows.
• Collaborate with AI engineering teams to deploy models into production.

Also expected

• Create training, validation, and testing datasets.
• Perform model benchmarking, A/B testing, and performance analysis.
• Fine-tune foundation models when required.
• Implement model monitoring, observability, and ongoing evaluation processes.

Must-Have Experience & Skills

• 5–10 years of experience in data science, machine learning, or related applied ML roles.
• Strong Python programming skills, with Pandas, NumPy, and Scikit-learn.
• Strong foundation in supervised/unsupervised learning, statistical modeling, feature engineering, and model evaluation techniques.
• Hands-on Generative AI experience with LLM evaluation, prompt engineering, RAG architectures, embedding models, fine-tuning approaches, and agent evaluation frameworks.
• Experience with PyTorch and/or TensorFlow.
• Exposure to OpenAI models, Claude, Gemini, and/or open-source LLMs.
• Experience with vector databases, semantic search, and retrieval optimization.
• Experience delivering or supporting production AI/ML solutions in enterprise environments.
• Experience working with distributed onshore/offshore teams.

Preferred Skills

• Databricks, MLflow, and Spark.
• GraphRAG and Knowledge Graphs; exposure to Neo4j.
• Responsible AI, Explainable AI, and AI governance.
• Banking or Financial Services domain experience.
• Familiarity with Azure AI Foundry, AWS Bedrock, Kubernetes, and AI observability platforms.

Soft Skills

• Clear communication with engineering and business stakeholders.
• Ability to translate evaluation results into actionable model/product decisions.
• Comfortable owning quality metrics and trade-offs (accuracy, latency, cost, risk) in a delivery setting.