{"id":928530,"url":"https://alion.io/job/synechron-ai-tech-lead","title":"AI Engineer – Python, Generative AI, LLM/RAG, Cloud AI & MLOpsMLOps & Kubernetes","company":{"id":5306,"name":"Synechron","domain":"synechron.com","url":"https://alion.io/company/synechron","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":96,"open_postings":24,"ghost_share":0.042,"stale_share":0.167,"repost_share":0.167,"time_to_fill_p50_days":21,"computed_at":"2026-11-09T06:03:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":15000,"max_usd":31000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-09-15T09:40:46Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-09T22:17:03Z","board_verified":true,"closed_at":null,"days_open":55,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":54},"description":"Job Summary\nSynechron 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.\nThe 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.\nThis 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.\nSoftware Requirements\nRequired\nPython: Strong proficiency for AI/ML development, data processing, model development and production applications; experience with the project-supported version.\nTensorFlow, PyTorch and Scikit-learn: Hands-on experience developing, optimizing and evaluating machine learning and deep learning models.\n Generative AI and LLMs: Experience developing LLM-based applications, including prompt engineering, Retrieval-Augmented Generation (RAG) and AI agents.\nCloud AI Services: Hands-on experience with one or more of the following:Azure AIAzure OpenAIAWS AI/ML servicesGoogle Cloud AI services\nVector Databases: Experience storing, indexing and retrieving embeddings for AI and RAG applications.\nLangChain, Semantic Kernel or Similar Frameworks: Practical experience building LLM applications, orchestration workflows or AI agents.\nAPIs and Microservices: Experience designing or integrating APIs and microservices for AI-enabled applications.\nDocker and Kubernetes: Experience containerizing, deploying and managing AI/ML applications.\nCI/CD Pipelines: Experience integrating software and model delivery into automated build, test and deployment pipelines.\nSQL and NoSQL Databases: Experience working with structured and unstructured data stores.\nLarge-Scale Data Processing: Experience developing or supporting data pipelines for AI/ML solutions.\nMLOps: Experience with model deployment, monitoring, versioning, reliability and operational support.\nPreferred\nExperience with AI governance, responsible AI practices and model monitoring.\nExperience leading technical teams or AI/ML projects.\nCertification in Azure AI, AWS Machine Learning or equivalent cloud technologies.\nExperience with enterprise-scale Generative AI platforms and production LLM applications.\nExperience with model optimization, evaluation frameworks, observability and cost management.\nExperience implementing reusable AI platforms and shared services.\nOverall Responsibilities\nLead the design, development and deployment of AI/ML and Generative AI solutions.\nArchitect scalable AI platforms that meet functional, performance, security, reliability and maintainability requirements.\nCollaborate with business and technical teams to translate requirements into practical AI-driven solutions.\nDevelop, optimize and evaluate machine learning models, LLM-based applications, AI agents and data pipelines.\nDesign RAG solutions using embeddings, vector databases, retrieval strategies and prompt engineering.\nDrive technical discussions, code reviews and solution architecture decisions.\nEstablish development standards, reusable components, coding practices and engineering controls for AI solutions.\nMentor and guide AI/ML engineers and developers through technical coaching, design reviews and delivery support.\nEnsure model performance, reliability, security and scalability in production environments.\nSupport model deployment, monitoring, versioning, incident resolution and continuous improvement through MLOps practices.\nIntegrate AI capabilities with APIs, microservices, databases and enterprise applications.\nUse Docker, Kubernetes and CI/CD pipelines to support repeatable and controlled delivery.\nAssess emerging AI technologies and industry trends for their relevance to Synechron’s products and delivery objectives.\nSupport responsible AI, model governance, data protection, explainability and appropriate human oversight.\nManage technical risks, dependencies, delivery priorities and architectural trade-offs.\nPromote sustainable AI engineering by considering compute efficiency, model utilization, reuse, infrastructure optimization and long-term maintainability.\nTechnical Skills (By Category)\nProgramming Languages\nEssential\nStrong proficiency in Python for AI/ML development, model implementation, data processing and automation.\nAbility to write maintainable, testable and production-ready software.\nAbility to develop supporting services, integrations and utilities for AI-enabled applications.\nPreferred\nExperience with additional programming languages used in microservices, APIs or enterprise application integration.\nExperience developing asynchronous, distributed or high-throughput AI services.\nDatabases and Data Management\nEssential\nExperience working with SQL and NoSQL databases.\nExperience designing and supporting data pipelines for large-scale data processing.\nUnderstanding of data preparation, data quality, feature engineering, data access and data lineage.\nExperience with vector databases and embedding-based retrieval.\nAbility to manage structured, unstructured and semi-structured data used by AI/ML applications.\nPreferred\nExperience with data lake, warehouse or distributed data-processing architectures.\nExperience with data governance, metadata management and data-quality monitoring.\nExperience optimizing vector search, indexing and retrieval performance.\nCloud Technologies\nEssential\nHands-on experience with at least one of the following:Azure AI or Azure OpenAIAWS AI/ML servicesGoogle Cloud AI services\nAbility to design, deploy and operate AI/ML workloads in cloud environments.\nUnderstanding of cloud scalability, availability, monitoring, access control and cost considerations.\nPreferred\nExperience designing multi-service or multi-environment AI platforms.\nExperience with cloud-based model deployment, managed AI services and infrastructure automation.\nExposure to cloud cost optimization for large-scale model usage and data processing.\nFrameworks and Libraries\nEssential\nTensorFlow, PyTorch and/or Scikit-learn for machine learning and deep learning development.\nLangChain, Semantic Kernel or a similar framework for LLM application and AI-agent development.\nExperience with Generative AI, LLMs, RAG and prompt engineering.\nExperience building and integrating APIs and microservices.\nUnderstanding of NLP concepts and deep learning techniques.\nPreferred\nExperience with LLM evaluation, fine-tuning, grounding, guardrails and response-quality measurement.\nExperience developing reusable orchestration components and AI-agent workflows.\nFamiliarity with model-serving frameworks and AI application observability tools.\nDevelopment Tools and Methodologies\nEssential\nDocker for containerization of AI/ML applications and services.\nKubernetes for deployment and management of containerized workloads.\nGit-based software development and source control practices.\nCI/CD pipelines for automated build, test, deployment and release management.\nMLOps practices covering model deployment, versioning, monitoring and operational support.\nCode reviews, technical design reviews, automated testing and software engineering best practices.\nAgile delivery and collaborative development methods.\nPreferred\nExperience leading technical delivery across multiple AI/ML workstreams.\nExperience with infrastructure-as-code, automated environment provisioning and release governance.\nExperience implementing monitoring for model performance, data drift, latency, availability and usage.\nSecurity Protocols\nEssential\nUnderstanding of secure AI application design, API security, access management and protection of sensitive data.\nAwareness of privacy, data handling, authentication, authorization and secure deployment requirements.\nAbility to assess security risks associated with LLMs, RAG solutions, AI agents, prompts and retrieved content.\nUnderstanding of responsible AI principles, model governance and appropriate human oversight.\nPreferred\nExperience implementing AI governance controls, model-risk management and auditability.\nExperience with controls for prompt injection, data leakage, unauthorized model access and unsafe AI outputs.\nFamiliarity with security monitoring and compliance requirements for cloud-hosted AI workloads.\nExperience Requirements\n6-8 years of experience in software development, artificial intelligence, machine learning or a closely related technical field.\nStrong hands-on experience developing AI/ML solutions using Python and frameworks such as TensorFlow, PyTorch or Scikit-learn.\nExperience with Generative AI, LLMs, RAG, prompt engineering and AI agents.\nExperience deploying or operating AI/ML solutions using Azure AI, Azure OpenAI, AWS AI/ML or Google Cloud AI services.\nExperience with NLP, deep learning, model deployment and MLOps.\nExperience with vector databases, LangChain, Semantic Kernel or similar frameworks.\nExperience with APIs, microservices, Docker, Kubernetes and CI/CD pipelines.\nExperience working with SQL and NoSQL databases and large-scale data-processing solutions.\nExperience leading technical teams, projects or solution delivery workstreams.\nExposure to AI governance, responsible AI and model monitoring.\nExperience delivering enterprise AI solutions in financial services, technology, consulting or another regulated or data-intensive industry.\nCandidates may qualify through equivalent practical experience in software engineering, data engineering, machine learning engineering, platform engineering or Generative AI development that demonstrates the required capabilities, even where their previous job title was different.\nDay-to-Day Activities\nDesign, develop and review AI/ML, Generative AI, LLM, RAG and AI-agent solutions, including supporting models, APIs, data pipelines and deployment components.\nCollaborate with business stakeholders, architects, engineers and delivery teams to clarify requirements, assess technical options and agree solution designs.\nConduct code reviews, technical discussions, model-performance reviews and production-readiness assessments while mentoring AI/ML engineers and developers.\nTrack technical risks, make architecture and implementation recommendations within the agreed governance framework, and support reliable, secure and scalable delivery.\nQualifications\nA bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Data Science or a related field is preferred; equivalent relevant professional experience may be considered.\nCertification in Azure AI, AWS Machine Learning or an equivalent cloud technology is preferred.\nPractical training or demonstrated experience in machine learning, Generative AI, LLMs, RAG, prompt engineering, AI agents and MLOps is required.\nTraining in secure software engineering, responsible AI, cloud architecture, model monitoring and data protection is preferred.\nCommitment to continuous professional development in emerging AI technologies, cloud services, model engineering, governance and production AI practices is expected.\nProfessional Competencies\nApplies critical thinking to evaluate business problems, select appropriate AI approaches, assess model limitations and resolve technical issues.\nLeads through clear technical direction, constructive feedback, mentoring, code reviews and effective collaboration across engineering and delivery teams.\nCommunicates architecture decisions, technical risks, model behavior, delivery progress and business impacts clearly to technical and non-technical stakeholders.\nAdapts to evolving AI technologies, cloud capabilities, frameworks, regulatory expectations and project requirements through continuous learning.\nIdentifies practical opportunities to 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