{"id":2872823,"url":"https://alion.io/job/ecolab-senior-ai-engineer-4","title":"Senior AI Engineer","company":{"id":1754712,"name":"Ecolab","domain":"ecolab.com","url":"https://alion.io/company/ecolab-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":77,"open_postings":127,"ghost_share":0,"stale_share":0.906,"repost_share":0,"time_to_fill_p50_days":26,"computed_at":"2026-10-23T05:54:15Z"}},"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":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":26000,"max_usd":53000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":38},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Datadog","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"GitHub","optional":false},{"name":"Google AI Studio","optional":false},{"name":"LangChain","optional":false},{"name":"LLM","optional":false},{"name":"New Relic","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Vertex AI","optional":false},{"name":"Weaviate","optional":false},{"name":"A2A","optional":true},{"name":"C#","optional":true},{"name":"Copilot Studio","optional":true},{"name":"Machine Learning","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-07-07T00:00:00Z","employer_posted_date":"2026-07-07","last_verified_at":"2026-10-23T04:40:01Z","board_verified":true,"closed_at":null,"days_open":108,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":108},"description":"Role Summary\nAs a Senior AI Engineer, you will design, build, deploy, and support production-grade GenAI and agentic AI solutions that integrate large language models (LLMs), retrieval-based patterns, APIs, and enterprise workflows. You will play a hands-on engineering role in delivering scalable, reliable, and maintainable AI-powered product capabilities, while partnering closely with Lead AI Engineers, Team Leads, architects, and cross-functional product teams.\nThis role is ideal for an engineer with strong technical depth in LLM-powered application development, RAG, and cloud-native AI delivery, who can independently implement solution components, contribute to engineering standards, and help operationalize AI systems in real business environments.\nKey Responsibilities\nDesign, develop, test, and deploy LLM-powered application components and AI-enabled services for enterprise use cases\nBuild and optimize retrieval-augmented generation (RAG) pipelines, including document ingestion, chunking, embeddings, retrieval strategies, and response grounding\nImplement agentic AI workflows using orchestration frameworks and reusable design patterns for task execution, tool usage, and context handling\nDevelop AI-enabled APIs and backend services using technologies such as Python, FastAPI, Azure Functions, containerized services, and REST-based integration patterns (or equivalent platforms and frameworks)\nWork with Azure OpenAI, Azure AI Studio, Semantic Kernel, LangChain, AutoGen, Azure AI Search, or equivalent tools to build scalable GenAI solutions\nCollaborate with Lead AI Engineers and architects to translate solution designs into robust technical implementations\nIntegrate AI services with enterprise systems, APIs, workflow platforms, and downstream applications\nImplement logging, tracing, monitoring, and basic operational controls using tools such as Application Insights, OpenTelemetry, Azure Monitor, Datadog, New Relic, or equivalent observability platforms\nParticipate in design reviews, code reviews, testing, and release activities to maintain quality and engineering discipline\nContribute to reusable assets such as prompt patterns, orchestration templates, shared components, developer utilities, and engineering accelerators\nTroubleshoot production issues, improve reliability, and support continuous improvement of deployed AI capabilities\nStay current with advancements in LLM tooling, agent frameworks, prompt engineering, retrieval approaches, and applied AI engineering practices\nRequired Qualifications\n5 to 8+ years of experience in software engineering, AI/ML engineering, or AI solution delivery, including hands-on work in building and deploying intelligent applications\nPractical experience delivering GenAI, LLM-powered, or AI-enabled solutions in development, pilot, or production environments\nStrong technical foundation in Python and modern backend engineering patterns, with experience building APIs, services, and application components\nHands-on experience with LLM platforms and AI development tools such as Azure OpenAI, Azure AI Studio, OpenAI API, AWS Bedrock, Google Vertex AI, or equivalent\nExperience working with orchestration frameworks such as Semantic Kernel, LangChain, AutoGen, or equivalent approaches for prompt workflows, tool calling, and agent coordination\nStrong working knowledge of retrieval-augmented generation (RAG), embeddings, vector search, and grounding patterns using platforms such as Azure AI Search, Pinecone, Weaviate, FAISS, or equivalent\nExperience building and deploying cloud-native AI services using tools such as Azure Functions, Azure Container Apps, FastAPI, Docker, GitHub, Azure DevOps, or equivalent engineering and deployment platforms\nSolid understanding of CI/CD, containerization, automated testing, and secure deployment practices for modern AI-enabled applications\nFamiliarity with observability and operational tooling such as Application Insights, OpenTelemetry, Azure Monitor, Datadog, or New Relic, or equivalent monitoring platforms\nExperience integrating AI services with REST APIs, enterprise workflows, backend systems, or downstream business applications\nStrong problem-solving skills and ability to translate solution requirements into well-structured technical implementations\nStrong ownership mindset across the SDLC, including design, build, testing, deployment, support, and continuous improvement\nGood collaboration and communication skills, with the ability to work effectively with engineers, architects, product owners, and platform teams\nPreferred Qualifications\nExperience implementing agentic AI workflows involving multi-step reasoning, tool orchestration, structured prompting, or reusable workflow patterns\nExposure to Model Context Protocol (MCP), agent-to-agent (A2A) interaction patterns, or similar approaches to context exchange and distributed agent communication\nFamiliarity with Microsoft AI Foundry, Azure Machine Learning, Azure AI / Copilot Studio, or equivalent enterprise AI experimentation and solution development platforms\nExperience with enterprise integrations, including workflow tools, API management layers, business systems, or event-driven architectures\nExperience contributing to reusable GenAI accelerators, prompt libraries, orchestration templates, internal developer tooling, or shared engineering utilities\nFamiliarity with AI governance, safety, evaluation, and cost-management practices, including token usage awareness, prompt safety, and quality monitoring\nWorking knowledge of TypeScript or C#, in addition to Python, for integration into broader enterprise technology stacks\nExperience operating in a build-own-operate product environment with expectations around supportability, reliability, and iterative enhancement\nAbility to clearly communicate technical decisions, implementation trade-offs, and design considerations to both technical and non-technical 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