Total 20 system engineer job vacancies in Pune

Role Overview

An AI Engineer at METRO unlocks the potential of AI for full organization. Rapidly delivers business-driven AI solutions by integrating existing cloud models and APIs, rather than inventing new algorithms. An AI Engineer builds digital agents and engineers the future of smart wholesale by delivering pragmatic, secure, maintainable, and observable AI powered solutions, that drive business value. Curious to get more information about the team, the products, or the tech stack?

Key Responsibilities

• Rapidly deliver business-driven AI solutions by integrating existing cloud models and APIs, focusing on practical implementation rather than developing new algorithms.
• Translate business goals into AI-powered solutions, ensuring technical feasibility through collaboration with squad leads, data scientists, architects, and full stack engineers.
• Integrate Google Cloud AI services and models into microservices and workflows to support scalable and maintainable solutions.
• Design, implement, deploy, and monitor intelligent agents to support automation and personalization.
• Build and maintain CI/CD pipelines using Cloud Build and GitHub Actions, collaborating closely with DevOps engineers and SREs for robust deployment and monitoring.
• Maintain comprehensive documentation including API references, runbooks, architecture diagrams, and conduct internal demos and training sessions.
• Ensure all AI solutions comply with METRO’s corporate policies on responsible AI and ethical standards.

Quadric delivers its GPNPU as soft IP — RTL and implementation collateral — enabling customers to integrate our processor into their own SoCs across a range of process nodes and foundries. You will drive PPA optimization across IP configurations, build the scalable reference flows customers use to evaluate and integrate our IP, and provide hands-on implementation support to customers working toward their tapeouts.

Responsibilities

PPA Optimization & Analysis

• Drive PPA analysis and optimization for Quadric GPNPU soft IP across process nodes and hardware configurations — timing, area, leakage, and dynamic power
• Apply low-power techniques (clock gating, multi-Vt, operand isolation) and synthesis/P&R knobs to hit frequency and area targets
• Characterize the IP design space across configurations and build PPA models that support customer evaluations and pre-sales engagements
• Partner with RTL and architecture teams early to quantify tradeoffs and influence design decisions before they become costly to reverse

Reference Flow Development

• Build and maintain a scalable RTL-to-GDS reference flow for Quadric soft IP that customers can use to evaluate, integrate, and close PPA in their own SoC environment
• Ensure the flow is portable across supported process nodes with clear BKMs, SDC templates, floorplan scripts, and integration guidelines
• Develop TCL and Python automation — and leverage AI coding tools such as Claude — to accelerate flow development, reduce manual effort, and improve repeatability
• Qualify EDA tool updates and benchmark QoR impact before rolling into the reference flow

Customer Integration & Tapeout Support

• Act as the primary PD contact for customers integrating Quadric soft IP, guiding them from evaluation through their SoC tapeout
• Help customers adapt the reference flow to their process node, foundry PDK, and internal design environment
• Triage and resolve customer-reported implementation issues — timing, congestion, power, or flow failures — working with internal teams to deliver fixes or updated collateral
• Support FAE and business development with PPA feasibility studies for new customer engagements

Collaboration & Documentation

• Work with architecture, RTL, and software teams to ensure IP deliverables meet customer-facing PPA targets
• Document methodologies, BKMs, and optimization learnings; maintain process node bring-up guidelines to support IP portability

Education & Experience

• BS/MS in Electrical Engineering, Computer Engineering, or related field
• 4+ years of ASIC or processor IP physical design experience focused on PPA optimization and flow development across advanced nodes

Technical Skills

• Proficiency with industry-standard physical design tools from Synopsys or Cadence (synthesis, place-and-route, and timing analysis)
• Experience with advanced FinFET process nodes (16nm and below); multi-node experience preferred
• Strong TCL scripting and Python automation skills
• Solid understanding of synthesis and P&R levers for PPA — timing paths, cell selection, congestion, and power intent
• Hands-on experience with low-power design techniques and MCMM timing analysis
• Comfort using AI tools (e.g., Claude, Copilot) to accelerate script development, automate repetitive EDA tasks, and improve workflow productivity
• Understanding of DFT concepts (scan, ICG bypass) and their physical design implications

Nice to Have

• Experience delivering soft IP to external customers or supporting SoC integrators through tapeout
• Background in AI accelerator, NPU, or DSP processor IP implementation
• Exposure to metrics-driven QoR tracking and large-scale synthesis run management

About Job:

Senior Software Development Engineer, Pune, India 

 Responsibilities:

• Understanding business requirements and how they fit into the overall, global solution design  
• Embracing end-to-end the development and maintenance of any new feature including the estimation, the development and validation of new code, the design of testing scenarios up to the deployment into production  
• Following source and test-driven development best practices  
• Troubleshooting and identifying root cause analysis while resolving the issues  

Sales of all Bosch Rexroth products for Agriculture segment in WZ
Regular customer visits & building excellent customer relationship
Generating new enquiries
Techno-commercial proposal offering & negotiation with customers
Driving new projects
Offer new concepts & products to grow market share
Responsible for creating excellent customer experience
Responsible for achieving all key KPI’s
Resolving customer quality topics on top priority

About Job:

AI Platform & Agentic Systems Engineer (AI / ML), Pune, India (Hybrid)

Experience- 5+ years 

We are looking for a high-impact AI Platform & Agentic Systems Engineer who will help drive the AI transformation across our organization.

This role goes beyond traditional machine learning development. The engineer will design and build AI-powered platforms, intelligent agents, orchestration layers, and enterprise AI workflows that enhance productivity, automate processes, and unlock intelligence across systems and data.

You will work extensively with LLMs, SLMs, agentic AI architectures, vector databases, RAG pipelines, orchestration frameworks, and enterprise integrations to build scalable AI-enabled solutions.

The ideal candidate is someone who actively builds with AI, understands modern AI ecosystems deeply, and can convert AI capabilities into real-world production systems.

Key Responsibilities

Enterprise AI Platform Development

• Build and maintain internal AI platforms and services that enable teams across the organization to leverage AI capabilities

• Develop reusable AI APIs, SDKs, and microservices for enterprise adoption

• Integrate AI capabilities into existing systems, developer workflows, and business platforms

• Enable organization-wide adoption of AI-assisted workflows and automation

Agentic AI & Autonomous Systems

• Design and implement agent-based architectures capable of multi-step reasoning and task execution

• Build autonomous AI agents that interact with APIs, enterprise tools, and data systems

• Develop multi-agent systems capable of collaboration and workflow orchestration

• Implement tool-using agents that can dynamically select and use enterprise services

Examples may include:

• AI engineering assistants
• workflow automation agents
• data intelligence agents
• internal knowledge agents

AI Orchestration & Workflow Engines

• Design AI orchestration layers that coordinate models, tools, and workflows

• Build pipelines that manage:

o multi-step reasoning

o task decomposition

o tool execution

o memory and context management

• Work with orchestration frameworks such as:

o LangChain

o LlamaIndex

o CrewAI

o similar orchestration frameworks

• Enable structured interaction between LLMs, enterprise tools, APIs, and knowledge systems

LLM / SLM Systems

• Work with modern Large Language Models (LLMs) and Small Language Models (SLMs)

• Evaluate and select models based on:

o performance

o latency

o cost

o reliability

• Implement advanced techniques such as:

o prompt engineering

o prompt chaining

o context window management

o function calling

o tool invocation

Retrieval-Augmented Generation (RAG)

• Design and implement RAG architectures for enterprise knowledge retrieval

• Build pipelines that integrate LLMs with internal knowledge bases

• Optimize retrieval systems to improve contextual accuracy and response relevance

• Implement indexing pipelines and semantic retrieval strategies

Vector Databases & Knowledge Infrastructure

• Work with vector databases and embedding systems to enable semantic search

• Implement knowledge indexing pipelines using:

o Pinecone

o Weaviate

o Milvus

o Chroma

• Build scalable knowledge retrieval layers for AI applications

Machine Learning & AI Model Development

• Build ML pipelines using Python-based machine learning stacks

• Work with ML frameworks including:

o PyTorch

o TensorFlow

o Scikit-learn

o Hugging Face ecosystem

• Develop data pipelines, feature engineering workflows, and model training pipelines

• Evaluate model performance and experiment with different architectures

AI Infrastructure & Model Deployment

• Deploy AI workloads in cloud and containerized environments

• Build model inference pipelines and scalable AI services

• Work with infrastructure tools such as:

o Docker

o Kubernetes

o cloud AI platforms

• Implement monitoring, evaluation, and lifecycle management for AI systems

AI Development Environment & Tooling

• Work within AI-enabled development environments

• Leverage modern AI tooling including:

o VS Code AI ecosystem

o Copilot

o AI coding assistants

• Build internal tools that improve AI productivity across engineering teams

AI Safety, Guardrails & Evaluation

• Implement mechanisms to ensure safe and reliable AI outputs

• Build guardrails to reduce hallucinations and unsafe responses

• Develop AI evaluation frameworks and benchmarks

• Monitor AI system performance and continuously improve reliability