Senior AI/ML Engineer

Jobtailor

Pune District

On-site

INR 3,000,000 - 6,000,000

Full time

3 days ago

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Job summary

Jobtailor in Pune, India, seeks an experienced AI/ML engineer to own GenAI solutions end to end, including data pipelines, models, prompts, and observability. You will design enterprise RAG platforms with secure ingestion, embeddings, and access-aware retrieval, and build production agentic systems with multi-agent orchestration and human-in-the-loop controls.

We expect strong fundamentals in Python, ML frameworks, and cloud deployment on AWS/Azure with Docker and Kubernetes.

Qualifications

Bachelor's or Master's in Computer Science, Data Science, AI/ML, Engineering, or related field — or equivalent practical experience
5–9 years developing production software, data, ML, or AI solutions, including hands-on delivery of GenAI/LLM applications
Advanced Python proficiency
Hands-on experience with ML frameworks (PyTorch/TensorFlow) and data libraries (Pandas/NumPy)
Strong grasp of LLM, agentic architecture, prompting, embeddings, RAG, tool calling, and human-in-the-loop controls
Experience designing APIs, distributed services, and secure AI tool execution
GIT/GitHub and CI/CD with automated build, test, security-scan, and deployment workflows
Cloud deployment on AWS/Azure and containerization with Docker; Kubernetes and IaC

Responsibilities

Own AI/ML and GenAI solutions end to end including data pipelines, models, prompts, APIs, evaluation and deployment
Design enterprise RAG platforms with secure ingestion, chunking, embeddings, and access-aware retrieval
Build production agentic systems with tool calling, memory, multi-agent orchestration and human-in-the-loop approvals
Architect and operate MCP clients/servers with secure transports, authentication and least-privilege access
Integrate agents with document repositories, ERP/CRM, databases, and cloud services through connectors
Define evaluation strategies and quality gates for accuracy, groundedness, safety, latency and cost
Establish end-to-end observability for agent and MCP activity
Build production services in Python with CI/CD and cloud deployment on AWS/Azure using Docker and Kubernetes
Mentor engineers and lead design/architecture reviews
Partner with product, architecture, data science, security, and business stakeholders

Skills

Advanced Python

GenAI/LLM Application Development

MCP Experience

LLM Architecture & Prompting

Leadership

Education

Bachelor's or Master's in Computer Science, Data Science, AI/ML, Engineering, or related field

Tools

Git/GitHub

CI/CD

Docker

Kubernetes

AWS

Azure

Pandas

NumPy

Scikit-Learn

PyTorch

TensorFlow

Job description

Own AI/ML and GenAI solutions end to end, including data pipelines, model and prompt workflows, APIs, evaluation, deployment, observability, and continuous optimization
Design enterprise RAG platforms with secure ingestion, chunking, embeddings, hybrid/vector search, reranking, citations, and access-aware retrieval
Build production agentic systems with tool/function calling, structured outputs, planning and memory, multi-agent orchestration, human-in-the-loop approvals, failure recovery, and auditable traces
Architect and operate MCP clients and servers exposing enterprise tools, resources, and prompts with secure transports, authentication, least-privilege access, tenant isolation, and protections against prompt injection and unsafe tool execution
Integrate agents with document repositories, source control, ticketing, databases, ERP/CRM, and cloud services through reusable connectors and governance patterns
Define evaluation strategies and quality gates for accuracy, groundedness, safety, latency, and cost
Establish end-to-end observability for agent and MCP activity
Build production services in Python with strong engineering practices, GitHub-based CI/CD, and cloud-native deployment on AWS or Azure using Docker, Kubernetes, and infrastructure as code
Partner with product, architecture, data science, security, and business stakeholders
Lead design and architecture reviews and mentor engineers

Requirements

Bachelor's or Master's in Computer Science, Data Science, AI/ML, Engineering, or a related field — or equivalent practical experience
5–9 years developing production software, data, ML, or AI solutions, including hands-on delivery of GenAI/LLM applications
Advanced Python
Practical experience with pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or equivalent
Strong grasp of LLM and agentic architecture, including prompting, context engineering, embeddings, RAG, tool/function calling, structured outputs, orchestration, evaluation, and human-in-the-loop controls
Experience designing APIs, distributed services, and event-driven or asynchronous workflows with secure tool execution for AI agents
Hands-on experience with Git/GitHub and CI/CD, including automated build, test, security-scan, and deployment workflows
Strong experience with AWS or Azure and containerized deployment using Docker
Kubernetes and infrastructure-as-code experience expected
Current, role-relevant AWS AI/ML certification or Microsoft Azure AI certification is mandatory
Hands-on production experience with Model Context Protocol (MCP) is mandatory
Experience with MLOps/LLMOps practices, including experiment tracking, model and prompt versioning, tracing, evaluation, monitoring, and cost optimization
Preferred: GenAI or agent frameworks, enterprise search and vector technologies, LLMOps/observability platforms, SQL and data modeling, streaming, workflow orchestration, data lakes/lakehouse platforms, enterprise AI domains, responsible AI, model risk management, data governance, privacy, regulatory/client compliance, and mentoring

Core Competencies

Demonstrates expertise in developing and deploying AI/ML solutions, with a strong focus on GenAI applications, data pipelines, and observability. Proficient in Python and cloud technologies, ensuring secure and efficient integration of enterprise tools and services.

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