{"id":3393632,"url":"https://alion.io/job/synechron-ai-engineer-2","title":"AI Engineer","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":22,"ghost_share":0,"stale_share":0.227,"repost_share":0.045,"time_to_fill_p50_days":20,"computed_at":"2026-10-30T05:48:23Z"}},"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":"2026-10-30T02:15:04Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Job Summary\nSynechron is seeking an AI Engineer to build and implement AI agents and AI architecture within CI/CD workflows on AWS.\nThe role will focus on developing AI-enabled solutions using LLMs, Generative AI, Python, RAG pipelines, MCP servers, vector databases, and AWS services. The AI Engineer will contribute to the design, development, integration, testing, deployment, and improvement of secure AI applications.\nThis position supports business objectives by enabling AI-driven automation, improving software engineering productivity, accelerating delivery workflows, supporting code generation and remediation, and delivering scalable AI capabilities within cloud-based environments.\nSoftware Requirements\nRequired Software Skills\nLLMs and Generative AI: Hands-on experience with OpenAI, Bedrock, or similar LLM and GenAI platforms.\nPython: Strong professional experience in Python and AI development, including application development, automation, API integration, testing, and deployment.\nRAG Pipeline Engineering: Hands-on experience designing and implementing RAG pipelines using SageMaker and Lambda.\nMCP Server Development: Experience developing MCP servers and integrating tools, APIs, services, and data sources.\nAgentic AI: Experience building agentic workflows or autonomous AI agents.\nLLM-Based Code Generation and Code Fixing: Exposure to using LLMs for code generation, code analysis, code fixing, transformation, or remediation.\nVector Databases: Experience with vector databases, embeddings, similarity search, indexing, metadata filtering, and retrieval.\nAWS Services: Good knowledge of Lambda, ECS/EKS, S3, and API Gateway.\nApplication Security: Understanding of application security principles, common vulnerabilities, secure coding, access controls, and protection of AI applications and data.\nPreferred Software Skills\nUnderstanding and exposure to CI/CD tools such as GitHub, Jenkins, Harness, or equivalent tools.\nUnderstanding and exposure to 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, vulnerability scanning, code quality checks, and deployment security gates.\nOverall Responsibilities\nBuild and implement AI agents and AI architecture within CI/CD workflows on AWS.\nDevelop production-ready AI applications using LLMs and Generative AI platforms.\nDesign, implement, and maintain RAG pipelines using SageMaker, Lambda, vector databases, embeddings, and retrieval components.\nDevelop MCP servers and integrate approved tools, APIs, services, and enterprise data sources.\nBuild agentic workflows and autonomous AI agents that support task planning, tool use, context management, and response validation.\nUse Python to develop scalable, maintainable, testable, and secure AI solutions.\nSupport code generation and code fixing using LLMs, including code recommendations, defect identification, and remediation workflows.\nIntegrate AI solutions with CI/CD workflows, source control, security tools, testing processes, and deployment pipelines.\nUse AWS services, including Lambda, ECS/EKS, S3, and API Gateway, to develop and operate AI applications.\nApply application security principles to AI architecture, APIs, data flows, prompts, tools, model interactions, and generated code.\nCollaborate with software engineering, DevOps, security, architecture, and business stakeholders.\nDocument AI architecture, data flows, system behavior, dependencies, security controls, and operational procedures.\nSupport efficient use of models, compute, storage, and cloud resources to promote sustainable technology delivery.\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 from approved data sources.\nMCP servers and tool integrations operate according to defined access, validation, and security controls.\nAI solutions meet agreed requirements for functionality, reliability, scalability, security, latency, and cost.\nLLM-based code generation and code-fixing capabilities support development productivity while maintaining appropriate review and quality controls.\nSecurity vulnerabilities and risks are identified, documented, prioritized, and addressed through appropriate processes.\nAI solutions include suitable testing, monitoring, documentation, and operational support procedures.\nTechnical Skills (By Category)\nProgramming Languages\nEssential\nStrong experience with Python for AI development, automation, API integration, data processing, and cloud application development.\nAbility to write modular, maintainable, testable, and secure Python code.\nExperience developing Python 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 technology environments.\nDatabases and Data Management\nEssential\nExperience with vector databases and vector search.\nUnderstanding of embeddings, document chunking, indexing, similarity search, metadata filtering, and retrieval optimization.\nExperience designing RAG data flows, including ingestion, preprocessing, storage, retrieval, and response grounding.\nUnderstanding of data security, access controls, retention, and protection of sensitive information.\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 Lambda, ECS/EKS, S3, and API Gateway.\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 with multi-environment deployment strategies.\nExperience optimizing cloud resource usage, model inference costs, storage, and compute consumption.\nFrameworks and Libraries\nEssential\nHands-on experience with LLMs and GenAI, including OpenAI, Bedrock, or similar platforms.\nExperience developing RAG pipelines using SageMaker and Lambda.\nExperience building 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, prompt management, and response-quality measurement.\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 applying software development lifecycle practices to Python and AI applications.\nUnderstanding of application architecture, API design, distributed systems, and cloud-native development.\nAbility to document AI architecture, agent behavior, tool permissions, dependencies, data flows, 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.\nExposure to 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 assessments, 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, data masking, audit logging, and model risk controls.\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, relevant technical training, open-source contributions, or a combination of applied experience and project delivery.\nDay-to-Day Activities\nDesign, develop, test, and deploy AI agents, RAG pipelines, MCP servers, and LLM-based applications using Python and AWS services.\nCollaborate with software engineering, DevOps, security, architecture, and business stakeholders to define requirements, review designs, and resolve implementation issues.\nIntegrate AI solutions with CI/CD workflows, vector databases, APIs, code repositories, security tools, and remediation systems.\nEvaluate solution performance, security, reliability, latency, cost, and response quality while making technical decisions within approved architecture and governance standards.\nQualifications\nA bachelor’s or master’s degree in Computer Science, Information Technology, Engineering, Artificial Intelligence, Data Science, or a related field is preferred; equivalent relevant experience may be considered.\nMinimum 8+ years of overall professional experience and at least 3+ years of relevant AI experience are required.\nAWS, AI, machine learning, cloud architecture, or 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