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 implementing monitoring for model performance, data drift, latency, availability and usage.
Security Protocols
Essential
Understanding of secure AI application design, API security, access management and protection of sensitive data.
Awareness of privacy, data handling, authentication, authorization and secure deployment requirements.
Ability to assess security risks associated with LLMs, RAG solutions, AI agents, prompts and retrieved content.
Understanding of responsible AI principles, model governance and appropriate human oversight.
Preferred
Experience implementing AI governance controls, model-risk management and auditability.
Experience with controls for prompt injection, data leakage, unauthorized model access and unsafe AI outputs.
Familiarity with security monitoring and compliance requirements for cloud-hosted AI workloads.
Experience Requirements