AI Engineer – Python, Generative AI, LLM/RAG, Cloud AI & MLOpsMLOps & Kubernetes

Job Summary

Synechron is seeking an AI Engineer to design, develop and the implementation of Artificial Intelligence, Machine Learning and Generative AI solutions. The role will combine hands-on software engineering with technical leadership across machine learning models, LLM-based applications, data pipelines, cloud AI services and production-grade AI platforms.

The AI Tech Lead will translate business and technical requirements into scalable AI-driven solutions, guide architecture decisions, mentor AI/ML engineers and collaborate with stakeholders to deliver reliable, secure and maintainable products.

This is a full-time position based in Pune, suitable for professionals with 6–8 years of experience in software development, artificial intelligence or machine learning.

Software Requirements

Required

• Python: Strong proficiency for AI/ML development, data processing, model development and production applications; experience with the project-supported version.

• TensorFlow, PyTorch and Scikit-learn: Hands-on experience developing, optimizing and evaluating machine learning and deep learning models.

• Generative AI and LLMs: Experience developing LLM-based applications, including prompt engineering, Retrieval-Augmented Generation (RAG) and AI agents.

• Cloud AI Services: Hands-on experience with one or more of the following:Azure AIAzure OpenAIAWS AI/ML servicesGoogle Cloud AI services

• Vector Databases: Experience storing, indexing and retrieving embeddings for AI and RAG applications.

• LangChain, Semantic Kernel or Similar Frameworks: Practical experience building LLM applications, orchestration workflows or AI agents.

• APIs and Microservices: Experience designing or integrating APIs and microservices for AI-enabled applications.

• Docker and Kubernetes: Experience containerizing, deploying and managing AI/ML applications.

• CI/CD Pipelines: Experience integrating software and model delivery into automated build, test and deployment pipelines.

• SQL and NoSQL Databases: Experience working with structured and unstructured data stores.

• Large-Scale Data Processing: Experience developing or supporting data pipelines for AI/ML solutions.

• MLOps: Experience with model deployment, monitoring, versioning, reliability and operational support.

Preferred

• Experience with AI governance, responsible AI practices and model monitoring.

• Experience leading technical teams or AI/ML projects.

• Certification in Azure AI, AWS Machine Learning or equivalent cloud technologies.

• Experience with enterprise-scale Generative AI platforms and production LLM applications.

• Experience with model optimization, evaluation frameworks, observability and cost management.

• Experience implementing reusable AI platforms and shared services.

Overall Responsibilities

• Lead the design, development and deployment of AI/ML and Generative AI solutions.

• Architect scalable AI platforms that meet functional, performance, security, reliability and maintainability requirements.

• Collaborate with business and technical teams to translate requirements into practical AI-driven solutions.

• Develop, optimize and evaluate machine learning models, LLM-based applications, AI agents and data pipelines.

• Design RAG solutions using embeddings, vector databases, retrieval strategies and prompt engineering.

• Drive technical discussions, code reviews and solution architecture decisions.

• Establish development standards, reusable components, coding practices and engineering controls for AI solutions.

• Mentor and guide AI/ML engineers and developers through technical coaching, design reviews and delivery support.

• Ensure model performance, reliability, security and scalability in production environments.

• Support model deployment, monitoring, versioning, incident resolution and continuous improvement through MLOps practices.

• Integrate AI capabilities with APIs, microservices, databases and enterprise applications.

• Use Docker, Kubernetes and CI/CD pipelines to support repeatable and controlled delivery.

• Assess emerging AI technologies and industry trends for their relevance to Synechron’s products and delivery objectives.

• Support responsible AI, model governance, data protection, explainability and appropriate human oversight.

• Manage technical risks, dependencies, delivery priorities and architectural trade-offs.

• Promote sustainable AI engineering by considering compute efficiency, model utilization, reuse, infrastructure optimization and long-term maintainability.

Technical Skills (By Category)

Programming Languages

Essential

• Strong proficiency in Python for AI/ML development, model implementation, data processing and automation.

• Ability to write maintainable, testable and production-ready software.

• Ability to develop supporting services, integrations and utilities for AI-enabled applications.

Preferred

• Experience with additional programming languages used in microservices, APIs or enterprise application integration.

• Experience developing asynchronous, distributed or high-throughput AI services.

Databases and Data Management

Essential

• Experience working with SQL and NoSQL databases.

• Experience designing and supporting data pipelines for large-scale data processing.

• Understanding of data preparation, data quality, feature engineering, data access and data lineage.

• Experience with vector databases and embedding-based retrieval.

• Ability to manage structured, unstructured and semi-structured data used by AI/ML applications.

Preferred

• Experience with data lake, warehouse or distributed data-processing architectures.

• Experience with data governance, metadata management and data-quality monitoring.

• Experience optimizing vector search, indexing and retrieval performance.

Cloud Technologies

Essential

• Hands-on experience with at least one of the following:Azure AI or Azure OpenAIAWS AI/ML servicesGoogle Cloud AI services

• Ability to design, deploy and operate AI/ML workloads in cloud environments.

• Understanding of cloud scalability, availability, monitoring, access control and cost considerations.

Preferred

• Experience designing multi-service or multi-environment AI platforms.

• Experience with cloud-based model deployment, managed AI services and infrastructure automation.

• Exposure to cloud cost optimization for large-scale model usage and data processing.

Frameworks and Libraries

Essential

• TensorFlow, PyTorch and/or Scikit-learn for machine learning and deep learning development.

• LangChain, Semantic Kernel or a similar framework for LLM application and AI-agent development.

• Experience with Generative AI, LLMs, RAG and prompt engineering.

• Experience building and integrating APIs and microservices.

• Understanding of NLP concepts and deep learning techniques.

Preferred

• Experience with LLM evaluation, fine-tuning, grounding, guardrails and response-quality measurement.

• Experience developing reusable orchestration components and AI-agent workflows.

• Familiarity with model-serving frameworks and AI application observability tools.

Development Tools and Methodologies

Essential

• Docker for containerization of AI/ML applications and services.

• Kubernetes for deployment and management of containerized workloads.

• Git-based software development and source control practices.

• CI/CD pipelines for automated build, test, deployment and release management.

• MLOps practices covering model deployment, versioning, monitoring and operational support.

• Code reviews, technical design reviews, automated testing and software engineering best practices.

• Agile delivery and collaborative development methods.

Preferred

• Experience leading technical delivery across multiple AI/ML workstreams.

• Experience with infrastructure-as-code, automated environment provisioning and release governance.

• Experience i