Pay: Salary not listed in the saved posting
Work setup: Hybrid — work setup source
Work setup in the employer’s listing: hybrid
From the employer’s posting
Employment: Unconfirmed

What you’ll work on

Design and implement LLM-based and agentic AI solutions for business problems
Lead deployment and operation of production AI/ML and GenAI models on Azure, AWS or GCP
Run MLOps and LLMOps practices, including CI/CD, monitoring and evaluation
Build and apply RAG architectures, embeddings and vector search
Mentor engineers and provide technical leadership to cross-functional teams

From the employer’s posting

Design and implement advanced machine learning and AI solutions, including LLM-based applications and agentic AI systems

Lead the development, deployment, and operation of production AI/ML and GenAI models across major cloud platforms such as Azure, AWS, or GCP

Establish and maintain MLOps and LLMOps practices, including CI/CD pipelines, model lifecycle management, experiment tracking, evaluation, and monitoring

Develop and apply RAG architectures, embeddings, vector databases, prompt engineering, and LLM evaluation frameworks

Mentor engineers and provide technical leadership, collaborating with cross-functional teams

What you’ll bring

All qualifications

Core experience

Proficiency in SQL and experience with large-scale structured and unstructured datasets
Hands-on experience building and deploying LLM-based applications
Expertise in agentic AI including agent orchestration, autonomous workflows, tool/function calling, planning, task decomposition, memory, and context management
Experience with MLOps and LLMOps, including CI/CD, model lifecycle management, experiment tracking, and production deployment
Proven experience deploying and operating AI/ML or GenAI models/applications in Azure, AWS, or GCP
Expertise in RAG architectures, embeddings, vector databases/vector search, prompt engineering, and LLM evaluation

Preferred experience

Experience building enterprise-grade agentic AI platforms or multi-agent systems
Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Information Technology, or a closely related discipline
Expertise with Databricks Model Serving, Vector Search, MLflow, Unity Catalog, and related Databricks AI/ML capabilities
Experience with Kubernetes, Docker, REST APIs, microservices, and CI/CD pipelines

Qualification wording

Proficiency in SQL and experience with large-scale structured and unstructured datasets

Hands-on experience building and deploying LLM-based applications

Expertise in agentic AI including agent orchestration, autonomous workflows, tool/function calling, planning, task decomposition, memory, and context management

Experience with MLOps and LLMOps, including CI/CD, model lifecycle management, experiment tracking, and production deployment

Proven experience deploying and operating AI/ML or GenAI models/applications in Azure, AWS, or GCP

Expertise in RAG architectures, embeddings, vector databases/vector search, prompt engineering, and LLM evaluation

Experience building enterprise-grade agentic AI platforms or multi-agent systems

Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Information Technology, or a closely related discipline

Expertise with Databricks Model Serving, Vector Search, MLflow, Unity Catalog, and related Databricks AI/ML capabilities

Experience with Kubernetes, Docker, REST APIs, microservices, and CI/CD pipelines

Education & alternatives

Desired Qualifications: - Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Information Technology, or a closely related discipline - Certification in machine learning, AI engineering, or data science from a recognized institution such as TensorFlow Developer Certificate or Databricks Certified Professional Data Scientist

Tools in this posting

Python
SQL
AWS
Azure
Databricks
Docker
Google Cloud (GCP)
Kubernetes
MLflow
TensorFlow

Source

Required Skills: - Advanced hands-on programming experience in Python - Proficiency in SQL and experience with large-scale structured and unstructured datasets

- Advanced hands-on programming experience in Python - Proficiency in SQL and experience with large-scale structured and unstructured datasets - Strong practical understanding of machine learning and AI fundamentals

- Design and implement advanced machine learning and AI solutions, including LLM-based applications and agentic AI systems, to address complex business challenges and deliver measurable business outcomes - Lead the development, deployment, and operation of production AI/ML and GenAI models across major cloud platforms such as Azure, AWS, or GCP, ensuring high availability and scalability - Build, orchestrate, and optimize AI agents and autonomous workflows, focusing on robust memory, context management, and multi-agent architectures

- Experience with MLOps and LLMOps, including CI/CD, model lifecycle management, experiment tracking, and production deployment - Proven experience deploying and operating AI/ML or GenAI models/applications in Azure, AWS, or GCP - Expertise in RAG architectures, embeddings, vector databases/vector search, prompt engineering, and LLM evaluation

- Experience with Kubernetes, Docker, REST APIs, microservices, and CI/CD pipelines - Experience with managed GenAI platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AI - Experience with vector databases like Pinecone, Azure AI Search, Weaviate, or Databricks Vector Search

- Certification in machine learning, AI engineering, or data science from a recognized institution such as TensorFlow Developer Certificate or Databricks Certified Professional Data Scientist - Certification in cloud platforms or MLOps, for example AWS Certified Machine Learning Specialist or Azure AI Engineer Associate Employment type

- Experience with managed GenAI platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AI - Experience with vector databases like Pinecone, Azure AI Search, Weaviate, or Databricks Vector Search - Experience optimizing LLM applications for latency, throughput, scalability, token consumption, and cost

- Build, orchestrate, and optimize AI agents and autonomous workflows, focusing on robust memory, context management, and multi-agent architectures - Drive enterprise AI/ML workloads within the Databricks ecosystem, leveraging Databricks AI Agents, Model Serving, Vector Search, MLflow, and Unity Catalog to enhance operational efficiency - Establish and maintain MLOps and LLMOps practices, including CI/CD pipelines, model lifecycle management, experiment tracking, evaluation, and monitoring for continuous improvement

- Expertise in agentic AI including agent orchestration, autonomous workflows, tool/function calling, planning, task decomposition, memory, and context management - Extensive hands-on experience with Databricks AI Agents and the Databricks ecosystem - Experience with MLOps and LLMOps, including CI/CD, model lifecycle management, experiment tracking, and production deployment

- Experience building enterprise-grade agentic AI platforms or multi-agent systems - Expertise with Databricks Model Serving, Vector Search, MLflow, Unity Catalog, and related Databricks AI/ML capabilities - Experience with Kubernetes, Docker, REST APIs, microservices, and CI/CD pipelines

- Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Information Technology, or a closely related discipline - Certification in machine learning, AI engineering, or data science from a recognized institution such as TensorFlow Developer Certificate or Databricks Certified Professional Data Scientist - Certification in cloud platforms or MLOps, for example AWS Certified Machine Learning Specialist or Azure AI Engineer Associate

- Expertise with Databricks Model Serving, Vector Search, MLflow, Unity Catalog, and related Databricks AI/ML capabilities - Experience with Kubernetes, Docker, REST APIs, microservices, and CI/CD pipelines - Experience with managed GenAI platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AI

Posting record

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Status in our records: Live
First seen by us: Aug 25, 2026 · 54d ago
Recorded sightings: 116
Last seen by us: Oct 17, 2026

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Job description

View original posting ↗

Data Science Lead

Job requirements

Experience Range: With at least 8 years of experience in AI/ML engineering, machine learning, data science, software engineering, or related fields Key Responsibilities:

Design and implement advanced machine learning and AI solutions, including LLM-based applications and agentic AI systems, to address complex business challenges and deliver measurable business outcomes
Lead the development, deployment, and operation of production AI/ML and GenAI models across major cloud platforms such as Azure, AWS, or GCP, ensuring high availability and scalability
Build, orchestrate, and optimize AI agents and autonomous workflows, focusing on robust memory, context management, and multi-agent architectures
Drive enterprise AI/ML workloads within the Databricks ecosystem, leveraging Databricks AI Agents, Model Serving, Vector Search, MLflow, and Unity Catalog to enhance operational efficiency
Establish and maintain MLOps and LLMOps practices, including CI/CD pipelines, model lifecycle management, experiment tracking, evaluation, and monitoring for continuous improvement
Develop and apply RAG architectures, embeddings, vector databases, prompt engineering, and LLM evaluation frameworks to improve model performance and reliability
Ensure AI security, responsible AI practices, data privacy, and effective mitigation of hallucination, prompt injection, and GenAI guardrails
Mentor engineers and provide technical leadership, collaborating with cross-functional teams to deliver scalable, enterprise-grade AI solutions

Required Skills:

Advanced hands-on programming experience in Python
Proficiency in SQL and experience with large-scale structured and unstructured datasets
Strong practical understanding of machine learning and AI fundamentals
Hands-on experience building and deploying LLM-based applications
Expertise in agentic AI including agent orchestration, autonomous workflows, tool/function calling, planning, task decomposition, memory, and context management
Extensive hands-on experience with Databricks AI Agents and the Databricks ecosystem
Experience with MLOps and LLMOps, including CI/CD, model lifecycle management, experiment tracking, and production deployment
Proven experience deploying and operating AI/ML or GenAI models/applications in Azure, AWS, or GCP
Expertise in RAG architectures, embeddings, vector databases/vector search, prompt engineering, and LLM evaluation
Proficiency with LLM and GenAI frameworks/orchestration tools such as LangChain, LangGraph, Semantic Kernel, or similar technologies

Preferred Skills:

Experience building enterprise-grade agentic AI platforms or multi-agent systems
Expertise with Databricks Model Serving, Vector Search, MLflow, Unity Catalog, and related Databricks AI/ML capabilities
Experience with Kubernetes, Docker, REST APIs, microservices, and CI/CD pipelines
Experience with managed GenAI platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AI
Experience with vector databases like Pinecone, Azure AI Search, Weaviate, or Databricks Vector Search
Experience optimizing LLM applications for latency, throughput, scalability, token consumption, and cost

Desired Qualifications:

Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Information Technology, or a closely related discipline
Certification in machine learning, AI engineering, or data science from a recognized institution such as TensorFlow Developer Certificate or Databricks Certified Professional Data Scientist
Certification in cloud platforms or MLOps, for example AWS Certified Machine Learning Specialist or Azure AI Engineer Associate

Employment type

Employee

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Location & working pattern

Bangalore, Karnataka, India

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