{"id":1329292,"url":"https://alion.io/job/ecolab-ai-engineering-manager","title":"AI Engineering Manager","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":80,"open_postings":106,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":13,"computed_at":"2026-09-30T05:45:00Z"}},"role":"Leadership","role_family":"Leadership","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","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":35000,"max_usd":92000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":22},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A2A","optional":false},{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AutoGen","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","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":"GitHub Actions","optional":false},{"name":"Google AI Studio","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"New Relic","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Tool Use","optional":false},{"name":"Weaviate","optional":false},{"name":"Hallucination","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"NLP","optional":true},{"name":"Recommender Systems","optional":true},{"name":"ServiceNow","optional":true}],"status":"live","first_seen_at":"2026-09-24T00:00:00Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-09-29T11:58:54Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Manager/Team Lead - Agentic AI, ML\nLocation: Bangalore (Hybrid)\nROLE SUMMARY\nAs Manager - AI Engineering, you will lead a multidisciplinary team of AI Engineers and Data Scientists delivering GenAI-powered solutions, agentic AI systems, LLM-integrated applications, machine learning models, and retrieval-enabled intelligent products. This role combines technical leadership, people management, delivery accountability, and product ownership. You will be responsible for ensuring that the team delivers scalable, secure, supportable, and business-relevant AI and ML solutions from discovery through production operation and continuous improvement.\nThis is not a lightweight line-management role. The successful candidate must be technically strong enough to guide senior engineers, data scientists, and technical leads, make sound architectural and implementation trade-offs, and raise the engineering bar of the team. You are expected to provide hands-on technical direction across LLM-powered systems, agent orchestration, RAG, ML model development, MLOps, cloud-native application delivery, integrations, observability, and SDLC discipline, while also building a culture of accountability, ownership, experimentation rigor, and engineering excellence.\nYou will work closely with Product Managers, Solution Architects, Lead AI Engineers, Lead Data Scientists, Platform teams, DevOps, Security, and business stakeholders to translate business goals into implementable engineering work and durable product outcomes.\nKEY RESPONSIBILITIES\nLead and mentor a team of AI Engineers and Data Scientists across varying experience levels, including senior engineers and technical leads, ensuring strong delivery quality, modeling rigor, engineering discipline, and technical growth\nOwn end-to-end delivery of GenAI, agentic AI, and ML-enabled solutions - from discovery, design, and backlog shaping through development, testing, deployment, monitoring, and continuous improvement\nGuide architectural and design decisions across LLM-powered systems, embeddings pipelines, retrieval-augmented generation (RAG), agent orchestration, tool-enabled workflows, ML model pipelines, APIs, and cloud-native application services\nOversee the design, development, evaluation, and operationalization of machine learning models for predictive, classification, recommendation, anomaly detection, forecasting, optimization, or other business use cases where applicable\nEnsure strong practices across feature engineering, experimentation, validation, model performance assessment, explainability, drift awareness, and MLOps readiness\nTranslate business and product priorities into executable engineering and data science plans, technical workstreams, model delivery milestones, and sustainable release outcomes\nReview and challenge implementation choices to ensure systems and models are scalable, secure, observable, cost-aware, and maintainable\nPartner with Product Managers and engineering leadership to prioritize work, manage technical dependencies, and align delivery with roadmap goals\nEstablish strong SDLC and build-own-operate practices within the team, including design reviews, code reviews, model reviews, automated testing, release readiness, production support, reliability improvement, and technical debt management\nDrive reuse and productivity by scaling frameworks, shared components, prompt templates, orchestration patterns, feature templates, modeling utilities, evaluation frameworks, and internal accelerators across the team\nPromote a culture of responsible AI and operational excellence, emphasizing security, token and cost governance, model safety, quality, observability, reproducibility, and supportability\nCoordinate with cloud platform, DevOps, Security, Integration, Architecture, and data teams to ensure enterprise readiness of all deployments\nOwn hiring, onboarding, coaching, performance development, and growth plans for both the AI Engineering and Data Science team members\nTrack and communicate KPIs related to delivery, GenAI adoption, model quality, latency, business impact, reliability, experimentation outcomes, and production performance\nAct as the primary technical and delivery escalation point for the team, helping remove blockers and resolve design, modeling, execution, and operational issues\nRequired Qualifications\n10+ years of experience in software engineering, AI / ML engineering, data science, solution engineering, or technology delivery, including strong experience building and operating production-grade intelligent systems\n4 to 5 + years of experience delivering or leading AI / ML / GenAI / LLM-powered solutions in enterprise or product environments\nProven experience leading multidisciplinary teams or technical pods delivering LLM-powered products, agentic AI workflows, machine learning models, or AI-enabled application capabilities, with accountability for both technical quality and delivery outcomes\nStrong technical depth in Python, modern backend engineering, machine learning solution delivery, API-first architectures, microservices, distributed systems, and cloud-native application delivery\nStrong hands-on or design-level experience with LLM platforms and orchestration frameworks such as Azure OpenAI, Azure AI Studio, Semantic Kernel, LangChain, AutoGen, or equivalent platforms used for enterprise GenAI delivery\nStrong experience designing or guiding implementations involving retrieval-augmented generation (RAG), embeddings pipelines, vector search, grounding strategies, and retrieval optimization using platforms such as Azure AI Search, Pinecone, Weaviate, FAISS, or equivalent\nStrong understanding of machine learning model development, including feature engineering, model training, validation, tuning, evaluation, performance interpretation, and production-readiness considerations\nPractical experience guiding or reviewing MLOps practices, including experiment tracking, model versioning, deployment automation, CI/CD for ML, monitoring, drift detection, retraining readiness, and reproducibility\nExperience building and deploying AI- and ML-enabled cloud-native services using technologies such as Azure Functions, Azure Container Apps, FastAPI, Docker, Azure DevOps, GitHub, GitHub Actions, Kubernetes / AKS, Azure Machine Learning, Databricks, MLflow, or equivalent engineering and deployment platforms\nStrong understanding of CI/CD, containerization, deployment automation, secure delivery practices, and operational readiness for AI-driven and ML-enabled systems\nKnowledge of Model Context Protocol (MCP), agent-to-agent (A2A) interaction models, memory / context management approaches, and other distributed AI coordination patterns\nPractical experience with observability and operational tooling such as Application Insights, Azure Monitor, OpenTelemetry, Log Analytics, Datadog, New Relic, or equivalent platforms, including monitoring of reliability, latency, cost, runtime behavior, and model / workflow health\nStrong understanding of agentic AI implementation patterns, including multi-step orchestration, tool calling, context management, and workflow decomposition\nExperience integrating AI- and ML-enabled solutions with REST APIs, enterprise systems, workflow platforms, event-driven services, or downstream business applications\nDemonstrated ability to translate business and product needs into scalable, secure, and maintainable AI / ML engineering solutions, while guiding teams on implementation trade-offs, experimentation choices, and delivery sequencing\nStrong SDLC ownership mindset across design, build, testing, deployment, support, reliability improvement, model lifecycle management, and long-term maintainability\nProven ability to raise engineering and data science quality through code reviews, model reviews, design guidance, architectural mentoring, coaching of senior engineers and data scientists, and reinforcement of reusable patterns and standards\nStrong people leadership capability, including coaching, feedback, performance management, capability development, and fostering accountability and engineering excellence\nStrong collaboration and communication skills, with the ability to work effectively across engineering, data science, product, platform, architecture, DevOps, and business stakeholders\nPreferred Qualifications\nExperience leading implementations involving agentic AI workflows, multi-agent coordination, tool-enabled automation, reusable orchestration abstractions, or structured task delegation patterns\nExperience with AI observability, prompt safety, runtime guardrails, hallucination mitigation, evaluation frameworks, quality monitoring, and enterprise governance practices for GenAI systems\nFamiliarity with broader enterprise AI and ML platforms such as Microsoft AI Foundry, Azure Machine Learning, PromptFlow, MLflow, Databricks, or equivalent AI / ML lifecycle and experimentation ecosystems\nExperience leading or supporting teams working on forecasting, optimization, recommender systems, anomaly detection, classification, NLP, or hybrid ML + GenAI solutions\nExperience contributing to reusable GenAI accelerators, internal SDKs, orchestration templates, prompt frameworks, feature templates, evaluation patterns, modeling utilities, or shared engineering utilities\nFamiliarity with enterprise integration landscapes involving SAP, ServiceNow, API management layers, workflow systems, event buses, and business process platforms\nExperience with cost-aware AI and ML delivery, including token usage visibility, model selection trade-offs, compute efficiency, scaling considerations, and engineering productivity optimization\nAbility to communicate complex technical and analytical decisions clearly to both engineers and non-technical stakeholders, and to represent team direction confidently in leadership forums\nExperience operating in a build-own-operate product environment\nKnowledge of responsible AI, AI quality engineering, governance-by-design, model risk awareness, and compliance-aware delivery in enterprise environments","description_format":"text","description_chars":10061,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Water Utilities","Chemical Manufacturing","Sterilization & Infection Control"],"lifecycle":[{"event":"open","at":"2026-09-27T07:58:05Z"}],"liveness":{"score":54,"band":"ok","label":"Likely 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