{"id":3924284,"url":"https://alion.io/job/7elevengsc-lead-engineer-ai","title":"Lead Engineer - AI","company":{"id":1047705,"name":"7Elevengsc","domain":"7-elevengsc.com","url":"https://alion.io/company/7elevengsc","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"iCIMS","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon EKS","optional":false},{"name":"API Gateway","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Go","optional":false},{"name":"Google GKE","optional":false},{"name":"IAM","optional":false},{"name":"Java","optional":false},{"name":"Kong","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Node JS","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"TypeScript","optional":false},{"name":"JavaScript","optional":true}],"status":"live","first_seen_at":"2026-11-07T17:35:15Z","employer_posted_date":null,"last_verified_at":"2026-11-07T17:35:15Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Job Description:\n\nWe are looking for a hands-on AI Lead Engineer who can lead both AI application development and AI platform engineering. The role will be responsible for building production-grade GenAI solutions and the cloud/platform capabilities required to deploy, scale, secure, and operate them across AWS and GCP.\n\nThe ideal candidate combines strong software engineering skills with practical experience in LLMs, RAG, AI agents, APIs, cloud, Kubernetes, and CI/CD.\n\nA hands-on technical leader who can take an AI solution from prototype to development to production to operations, while establishing reusable platform capabilities that allow engineering teams to build and deploy AI solutions faster and more securely.\n\nResponsibilities:\nDesign and develop production-grade GenAI applications, APIs, RAG solutions, AI agents, and MCP-based services.\nBuild reusable AI engineering patterns for LLM integration, prompt management, model routing, tool calling, evaluation, and guardrails.\nProficiency in backend development using any of these languages - Go (Golang), Java, TypeScript, Node.js, and Python, including API development, database integration, and building scalable, high-performance backend services.\nDesign and operate cloud-native AI workloads across AWS and GCP, using containers and Kubernetes.\nBuild CI/CD pipelines for AI applications, models, agents, APIs, and supporting services, including automated deployment and rollback.\nDevelop scalable AI platform capabilities for model serving, inference, artifact management, and agent/MCP deployment.\nImplement AI security and governance controls including authentication, authorization, secrets management, PII protection, prompt-injection protection, and data-leakage prevention.\nBuild observability for AI workloads covering latency, errors, token usage, model performance, and cost.\nWork with data and application teams to integrate vector databases, RAG pipelines, enterprise APIs, and data platforms.\nSupport API gateway and AI gateway capabilities including routing, rate limiting, authentication, retries, caching, and monitoring.\nDrive production reliability through scalability, high availability, disaster recovery, incident support, and operational best practices.\nProvide technical leadership, conduct architecture/design reviews, establish engineering standards, and mentor engineers.\n\nTechnical Skills Required:\n7+ years of experience in software engineering, AI engineering, platform engineering, DevOps, or SRE.\nStrong hands-on development experience with Python and REST APIs/microservices.\nProduction experience with Generative AI/LLMs, RAG, embeddings, AI agents, and model APIs.\nStrong experience with Docker, Kubernetes, Git, CI/CD, and Linux.\nHands-on experience with AWS and/or GCP cloud services and cloud-native architectures.\nExperience including model packaging, deployment, versioning, artifact management, and monitoring.\nExperience with observability, logging, monitoring, troubleshooting, and production operations.\nStrong understanding of cloud security, IAM, secrets management, and service-to-service authentication.\nExperience working with vector databases and enterprise data integration.\nStrong communication and technical leadership skills.\n\nEducation & Experience:\nExperience with AWS EKS / GCP GKE or equivalent managed Kubernetes platforms.\nExperience with AI agents, MCP, agent orchestration, or AI gateway architectures.\nExperience with Databricks, Airflow, or similar platforms.\nExperience with Apigee, Kong, AWS API Gateway, or similar API gateway technologies.\nExperience with Partner with data, AI, security, and application teams to enable vector databases, graph databases, feature stores, and RAG pipelines as reusable platform services.\nExperience with AI security, evaluation, governance, and cost optimization.\nSkills\nAI Agents, AWS, CI / CD, Docker, Embeddings, GCP, Gen AI, Kubernetes, LLM, Python, 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