Sr. Data Scientist (AI/ML, Deep learning)

AI Industry

Sr. Data Scientist (AI/ML, Deep learning)

Peak Hire Solutions

Company

Home

Sr. Data Scientist (AI/ML, Deep learning)

at AI Industry

Agency job

via Peak Hire Solutions

5 - 12 yrs

₹20L - ₹46L / yr

Mumbai, Bengaluru (Bangalore), Hyderabad, Gurugram

Skills

Data Science

Artificial Intelligence (AI)

Machine Learning (ML)

Generative AI

Deep Learning

Python

PyTorch

TensorFlow

JAX

Large Language Models (LLM)

Retrieval Augmented Generation (RAG)

MLOps

Docker

Kubernetes

Spark

CI/CD

RLHF (Reinforcement Learning from Human Feedback)

Large Language Models (LLM) tuning

Vector database

Review Criteria

Strong Senior Data Scientist (AI/ML/GenAI) Profile
5+ years of experience in designing, developing, and deploying Machine Learning / Deep Learning (ML/DL) systems in production
Must have strong hands-on experience in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX.
1+ years of experience in fine-tuning Large Language Models (LLMs) using techniques like LoRA/QLoRA, and building RAG (Retrieval-Augmented Generation) pipelines.
Must have experience with MLOps and production-grade systems including Docker, Kubernetes, Spark, model registries, and CI/CD workflows

Preferred

Prior experience in open-source GenAI contributions, applied LLM/GenAI research, or large-scale production AI systems
Preferred (Education) – B.S./M.S./Ph.D. in Computer Science, Data Science, Machine Learning, or a related field.

Job Specific Criteria

CV Attachment is mandatory
Which is your preferred job location (Mumbai / Bengaluru / Hyderabad / Gurgaon)?
Are you okay with 3 Days WFO?
Virtual Interview requires video to be on, are you okay with it?

Role & Responsibilities

Company is hiring a Senior Data Scientist with strong expertise in AI, machine learning engineering (MLE), and generative AI. You will play a leading role in designing, deploying, and scaling production-grade ML systems — including large language model (LLM)-based pipelines, AI copilots, and agentic workflows. This role is ideal for someone who thrives on balancing cutting-edge research with production rigor and loves mentoring while building impact-first AI applications.

Responsibilities:

Own the full ML lifecycle: model design, training, evaluation, deployment
Design production-ready ML pipelines with CI/CD, testing, monitoring, and drift detection
Fine-tune LLMs and implement retrieval-augmented generation (RAG) pipelines
Build agentic workflows for reasoning, planning, and decision-making
Develop both real-time and batch inference systems using Docker, Kubernetes, and Spark
Leverage state-of-the-art architectures: transformers, diffusion models, RLHF, and multimodal pipelines
Collaborate with product and engineering teams to integrate AI models into business applications
Mentor junior team members and promote MLOps, scalable architecture, and responsible AI best practices

Ideal Candidate

5+ years of experience in designing, deploying, and scaling ML/DL systems in production
Proficient in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX
Experience with LLM fine-tuning, LoRA/QLoRA, vector search (Weaviate/PGVector), and RAG pipelines
Familiarity with agent-based development (e.g., ReAct agents, function-calling, orchestration)
Solid understanding of MLOps: Docker, Kubernetes, Spark, model registries, and deployment workflows
Strong software engineering background with experience in testing, version control, and APIs
Proven ability to balance innovation with scalable deployment
B.S./M.S./Ph.D. in Computer Science, Data Science, or a related field
Bonus: Open-source contributions, GenAI research, or applied systems at scale

Review Criteria

Strong Senior Data Scientist (AI/ML/GenAI) Profile
5+ years of experience in designing, developing, and deploying Machine Learning / Deep Learning (ML/DL) systems in production
Must have strong hands-on experience in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX.
1+ years of experience in fine-tuning Large Language Models (LLMs) using techniques like LoRA/QLoRA, and building RAG (Retrieval-Augmented Generation) pipelines.
Must have experience with MLOps and production-grade systems including Docker, Kubernetes, Spark, model registries, and CI/CD workflows

Preferred

Prior experience in open-source GenAI contributions, applied LLM/GenAI research, or large-scale production AI systems
Preferred (Education) – B.S./M.S./Ph.D. in Computer Science, Data Science, Machine Learning, or a related field.

Job Specific Criteria

CV Attachment is mandatory
Which is your preferred job location (Mumbai / Bengaluru / Hyderabad / Gurgaon)?
Are you okay with 3 Days WFO?
Virtual Interview requires video to be on, are you okay with it?

Role & Responsibilities

Responsibilities:

Own the full ML lifecycle: model design, training, evaluation, deployment
Design production-ready ML pipelines with CI/CD, testing, monitoring, and drift detection
Fine-tune LLMs and implement retrieval-augmented generation (RAG) pipelines
Build agentic workflows for reasoning, planning, and decision-making
Develop both real-time and batch inference systems using Docker, Kubernetes, and Spark
Leverage state-of-the-art architectures: transformers, diffusion models, RLHF, and multimodal pipelines
Collaborate with product and engineering teams to integrate AI models into business applications
Mentor junior team members and promote MLOps, scalable architecture, and responsible AI best practices

Ideal Candidate

5+ years of experience in designing, deploying, and scaling ML/DL systems in production
Proficient in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX
Experience with LLM fine-tuning, LoRA/QLoRA, vector search (Weaviate/PGVector), and RAG pipelines
Familiarity with agent-based development (e.g., ReAct agents, function-calling, orchestration)
Solid understanding of MLOps: Docker, Kubernetes, Spark, model registries, and deployment workflows
Strong software engineering background with experience in testing, version control, and APIs
Proven ability to balance innovation with scalable deployment
B.S./M.S./Ph.D. in Computer Science, Data Science, or a related field
Bonus: Open-source contributions, GenAI research, or applied systems at scale

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