{"id":3393633,"url":"https://alion.io/job/synechron-ai-engineer-python-llmgenai-rag-aws-ai-agents","title":"AI Engineer – Python, LLM/GenAI, RAG, AWS & AI Agents","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":99,"open_postings":23,"ghost_share":0,"stale_share":0.217,"repost_share":0.043,"time_to_fill_p50_days":20,"computed_at":"2026-10-29T05:49:05Z"}},"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":17000,"max_usd":35000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":41},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"API Gateway","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GitHub","optional":false},{"name":"Hybrid Search","optional":false},{"name":"JavaScript","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Snyk","optional":false},{"name":"SonarQube","optional":false},{"name":"TypeScript","optional":false},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-10-28T11:28:41Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-10-29T08:27:02Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Job Summary\nSynechron is seeking an AI Engineer to build and implement AI agents and AI architecture within CI/CD workflows on AWS. The role will focus on developing production-ready generative AI solutions, retrieval-augmented generation (RAG) pipelines, agentic workflows, model integrations, and AI-enabled automation.\nThe AI Engineer will contribute to the design, development, deployment, and continuous improvement of AI solutions using Python, large language models (LLMs), AWS services, vector databases, and secure engineering practices. The role will also support code generation, code fixing, security automation, and remediation systems where applicable.\nThe position contributes to business objectives by accelerating software delivery, improving engineering productivity, enabling secure AI adoption, automating technical processes, and delivering scalable AI capabilities through reliable cloud-based architectures.\nSoftware Requirements\nRequired Software Skills\nPython: Strong professional experience in Python and AI development, including application design, API development, automation, testing, and production deployment.\nLLMs and Generative AI: Minimum 3 years of experience working with LLMs and GenAI, including OpenAI, Bedrock, or similar technologies.\nRAG pipeline engineering: Hands-on experience designing and implementing RAG pipelines using SageMaker, Lambda, vector databases, embeddings, document processing, retrieval, and response generation.\nMCP server development: Experience developing MCP servers and integrating tools, services, APIs, and data sources with AI applications.\nAgentic workflows: Experience building agentic workflows or autonomous AI agents, including task planning, tool use, orchestration, memory, workflow control, and response validation.\nCode generation and code fixing using LLMs: Exposure to using LLMs for code generation, code review, code transformation, defect identification, and code remediation.\nVector databases: Experience with vector databases, embedding generation, similarity search, metadata filtering, indexing, and retrieval optimization.\nAWS services: Good knowledge of AWS services, including Lambda, ECS/EKS, S3, API Gateway, and SageMaker.\nApplication security: Understanding of application security principles, common vulnerabilities, secure coding practices, identity and access controls, and protection of AI applications and data.\nCI/CD integration: Experience implementing or integrating AI agents and AI architecture within CI/CD workflows on AWS.\nPreferred Software Skills\nExposure to CI/CD tools such as GitHub, Jenkins, Harness, or equivalent tools.\nExperience with security tools such as Snyk, SonarQube, or equivalent tools.\nKnowledge of containerization and orchestration using Docker and Kubernetes.\nPrior work in security automation or remediation systems.\nExperience with automated testing, code quality checks, vulnerability scanning, and deployment gates.\nExperience integrating AI solutions with enterprise APIs, repositories, issue management platforms, and security tools.\nOverall Responsibilities\nDesign, develop, and implement AI agents and AI architecture within CI/CD workflows on AWS.\nBuild and maintain production-ready generative AI applications using LLMs, including OpenAI, Bedrock, or similar technologies.\nDevelop RAG pipelines using SageMaker, Lambda, vector databases, embeddings, document ingestion, retrieval, and response-generation components.\nDevelop MCP servers and integrate tools, APIs, services, data sources, and enterprise systems with AI applications.\nBuild agentic workflows and autonomous AI agents that can plan tasks, use approved tools, manage context, and produce validated outcomes.\nApply Python engineering practices to create maintainable, testable, observable, and scalable AI solutions.\nSupport code generation and code fixing using LLMs, including automated code recommendations, remediation workflows, and developer productivity capabilities.\nIntegrate AI solutions with CI/CD tools, source control, security tools, testing frameworks, and deployment workflows.\nUse AWS services, including Lambda, ECS/EKS, S3, API Gateway, and SageMaker, to deploy and operate AI applications.\nApply application security principles to AI architecture, data handling, API access, model interactions, prompts, tools, and generated code.\nEvaluate AI solution performance using measurable criteria such as accuracy, relevance, latency, reliability, cost, security, and operational stability.\nCollaborate with engineering, DevOps, security, architecture, and business stakeholders to define requirements and deliver AI capabilities.\nContribute to sustainable engineering practices by optimizing model usage, compute consumption, storage, retrieval processes, and cloud resource utilization.\nPerformance Outcomes and Expectations\nAI agents and AI architecture are implemented within defined CI/CD workflows on AWS.\nRAG pipelines provide relevant, reliable, secure, and traceable responses based on approved data sources.\nAI agents use tools and services according to defined permissions, validation controls, and operational policies.\nAI solutions meet agreed performance, security, scalability, cost, and maintainability criteria.\nCode generation and code-fixing capabilities improve development productivity while maintaining appropriate review and quality controls.\nVulnerabilities and security risks are identified, documented, prioritized, and addressed through appropriate automation or remediation processes.\nAI applications are supported by appropriate testing, monitoring, documentation, and operational procedures.\nTechnical Skills (By Category)\nProgramming Languages\nEssential\nStrong experience with Python for AI development, automation, API integration, data processing, and cloud-based application development.\nAbility to write maintainable, testable, modular, and secure Python code.\nExperience developing services, scripts, integrations, and automation for LLM and RAG applications.\nPreferred\nExperience with JavaScript or TypeScript for AI interfaces, service integrations, or tool development.\nExperience with shell scripting for automation and CI/CD activities.\nExperience working with multiple programming languages in enterprise software environments.\nDatabases and Data Management\nEssential\nExperience with vector databases and vector search.\nUnderstanding of embeddings, chunking, indexing, similarity search, metadata filtering, and retrieval optimization.\nExperience designing RAG data flows, including document ingestion, preprocessing, storage, retrieval, and response grounding.\nUnderstanding of data security, access controls, retention, and protection of sensitive information used by AI applications.\nPreferred\nExperience with relational and NoSQL databases.\nExperience with managed vector database services and hybrid search.\nKnowledge of data quality, data lineage, document versioning, and source validation.\nExperience implementing data ingestion and synchronization pipelines.\nCloud Technologies\nEssential\nGood knowledge of AWS services, including:LambdaECS/EKSS3API GatewaySageMaker\nExperience deploying AI applications and services in AWS environments.\nUnderstanding of AWS identity and access management, networking, logging, monitoring, scalability, and cost considerations.\nExperience implementing AI agents and AI architecture within AWS-based CI/CD workflows.\nPreferred\nExperience with additional AWS services for observability, messaging, secrets management, databases, and workflow orchestration.\nExperience with serverless and event-driven architectures.\nExperience implementing multi-environment deployment strategies.\nExperience optimizing cloud resource usage, model inference costs, storage, and compute consumption.\nFrameworks and Libraries\nEssential\nExperience with LLM and GenAI technologies, including OpenAI, Bedrock, or similar platforms.\nExperience developing RAG pipelines using SageMaker and Lambda.\nExperience with agentic workflows or autonomous AI agents.\nExperience developing MCP servers and tool integrations.\nExperience with Python libraries and frameworks for AI, data processing, APIs, testing, and automation.\nUnderstanding of prompt design, context management, response validation, and model evaluation.\nPreferred\nExperience with AI agent orchestration frameworks.\nExperience with model evaluation, tracing, observability, and prompt/version management.\nExperience with automated code generation, code fixing, code analysis, and software remediation using LLMs.\nExperience integrating AI applications with enterprise systems, APIs, repositories, security platforms, and workflow tools.\nDevelopment Tools and Methodologies\nEssential\nExperience integrating AI agents and AI architecture into CI/CD workflows.\nUnderstanding of source control, branching, pull requests, code reviews, automated testing, and deployment practices.\nExperience with Python-based software development lifecycle practices.\nUnderstanding of application architecture, API design, distributed systems, and cloud-native development.\nAbility to document AI architecture, data flows, agent behavior, tool permissions, dependencies, and operational procedures.\nExperience applying quality controls to AI-generated code and AI-enabled development processes.\nPreferred\nExposure to CI/CD tools such as GitHub, Jenkins, Harness, or equivalent tools.\nExperience with security tools such as Snyk, SonarQube, or equivalent tools.\nKnowledge of Docker and Kubernetes.\nExperience with security automation or remediation systems.\nExperience with automated vulnerability scanning, software composition analysis, static analysis, and security gates.\nExperience with DevSecOps, infrastructure automation, and release governance.\nSecurity Protocols\nEssential\nUnderstanding of application security principles and common application vulnerabilities.\nKnowledge of secure coding, input validation, output validation, dependency management, and secrets protection.\nUnderstanding of authentication, authorization, identity and access management, and least-privilege controls.\nAbility to secure LLM applications, RAG pipelines, MCP servers, APIs, prompts, tools, and generated code.\nAwareness of risks such as prompt injection, data leakage, insecure tool execution, excessive permissions, malicious content, and untrusted model output.\nExperience applying security reviews, vulnerability assessment, and remediation practices to AI and software solutions.\nPreferred\nExperience using Snyk, SonarQube, or equivalent security and code quality tools.\nPrior work in security automation or remediation systems.\nExperience integrating security checks into CI/CD pipelines.\nKnowledge of AI security testing, model risk controls, data masking, and audit logging.\nExperience Requirements\nMinimum 8+ years of overall professional experience in software engineering, cloud engineering, AI engineering, data engineering, or related technology roles.\nMinimum 3+ years of AI experience, including hands-on work with LLMs, GenAI, Python, RAG pipelines, AI agents, or related AI application development.\nDemonstrated experience building and implementing AI agents and AI architecture within CI/CD workflows on AWS.\nHands-on experience with LLMs and GenAI, including OpenAI, Bedrock, or similar technologies.\nExperience with RAG pipeline engineering using SageMaker, Lambda, vector databases, embeddings, and retrieval systems.\nExperience with MCP server development, tool integrations, agentic workflows, or autonomous AI agents.\nExperience with AWS services including Lambda, ECS/EKS, S3, and API Gateway.\nExperience applying application security principles to software, cloud, AI, API, and data solutions.\nPreferred experience includes CI/CD tools, security tools, Docker, Kubernetes, security automation, and remediation systems.\nCandidates may qualify through equivalent professional experience, demonstrable production AI implementations, substantial open-source contributions, techn...","description_format":"text","description_chars":15656,"description_truncated":true,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity","Flexible schedule","Professional 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