{"id":3675414,"url":"https://alion.io/job/schneider-electric-ai-engineering-lead","title":"AI Engineering Lead","company":{"id":2843582,"name":"Schneider Electric","domain":"schneider-electric.co.in","url":"https://alion.io/company/schneider-electric-co-in","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"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":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon CloudWatch","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":"AWS Step Functions","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"GitHub Actions","optional":false},{"name":"IAM","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"MLFlow","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Terraform","optional":false},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-11-03T10:35:20Z","employer_posted_date":null,"last_verified_at":"2026-11-03T10:35:20Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"At Schneider Electric's Digital Technology Centres (DTCs), we are building a next-generation enterprise AI Delivery team and are seeking an experienced AI Engineering Lead who combines deep, hands-on AI engineering expertise with technical leadership and people-development capabilities. You will remain closely involved in architecture, solution design, engineering reviews, productionisation, and resolution of complex technical challenges while leading a team of AI/ML Engineers. The role is centred on building and operating secure, scalable enterprise AI solutions, setting engineering direction, and ensuring technical quality from discovery through production.\n\nThe ideal candidate will have 8-12 years of experience spanning software engineering, cloud engineering, AI/ML systems, and MLOps, including 2+ years of technical or people leadership. They should bring strong AWS-based AI engineering depth, sound architectural judgement, and the ability to guide engineers while personally contributing to critical designs, reviews, prototypes, and production-readiness decisions.\n\nThe candidate will have responsibilities across the following functions: \n\nTechnical Leadership and Architecture: \nAct as a hands-on technical lead for the architecture, design, implementation, and productionisation of scalable, secure, reliable, and cost-efficient AI/ML and Generative AI solutions on AWS.\nContribute directly to solution design, technical spikes, reference implementations, code and design reviews, troubleshooting, performance optimisation, and production-readiness assessments.\nDefine engineering standards, architecture principles, reusable components, development frameworks, and best practices for AI solution delivery.\nDesign and guide the development of cloud-native applications, APIs, microservices, RAG solutions, agentic AI systems, and enterprise AI platforms.\nDrive MLOps and LLMOps adoption, including experimentation, model and prompt versioning, evaluation, deployment, monitoring, governance, observability, and lifecycle management.\nEstablish and improve CI/CD, Infrastructure-as-Code, containerisation, automated testing, release management, and platform automation practices.\nMake and review key architectural decisions across model integration, data and feature pipelines, application patterns, deployment approaches, scalability, resilience, security, and cost.\nDiagnose complex engineering issues and provide practical technical direction during development, testing, deployment, and live operations.\nEnsure enterprise-grade security, privacy, reliability, responsible AI, and compliance across AI solutions.\nEvaluate emerging AI technologies and AWS capabilities, validate them through hands-on experimentation, and translate them into pragmatic engineering roadmaps and reusable patterns.\n\nPeople Leadership and Talent Development: \nLead, mentor, and develop a team of AI Engineers through regular coaching, technical guidance, and constructive feedback.\nSet clear goals and ownership, support performance and career-development discussions, and maintain accountability for delivery and engineering quality.\nSupport workforce planning, interviewing, hiring, onboarding, and role-based capability development as the team scales.\nFoster a collaborative, inclusive, high-performing engineering culture built on learning, ownership, innovation, and continuous improvement.\n\nDelivery and Stakeholder Management: \nOwn the technical delivery of AI initiatives from discovery through production and ongoing operations.\nPartner with business, product, architecture, data, cloud, cybersecurity, and governance teams to align priorities and execute AI roadmaps.\nManage capacity, dependencies, timelines, risks, quality, and delivery governance while removing blockers.\nCommunicate progress and technical trade-offs, ensure measurable business value, and promote reuse of common AI capabilities.\n\nRequirements: \n8-12 years of experience in software engineering, cloud engineering, AI/ML engineering, data science, or related technology domains.\n2+ years of experience in technical leadership or people leadership, with demonstrated ownership of architecture, engineering quality, delivery, and team development.\nStrong hands-on expertise in Python, APIs, object-oriented design, testing practices, code quality, and modern software engineering principles.\nDeep experience with AWS cloud services and cloud-native architecture, including Amazon Bedrock, Amazon SageMaker AI, AWS Lambda, Amazon ECS or EKS, Amazon S3, Amazon API Gateway, AWS Step Functions, Amazon CloudWatch, AWS IAM, and related services.\nExperience designing, building, and operating production-grade AI/ML and Generative AI systems on AWS.\nStrong knowledge of ML lifecycle management, model serving, feature and data pipelines, experiment tracking, model monitoring, drift detection, observability, and retraining patterns.\nExperience with LLM applications, prompt engineering, RAG, embeddings, vector databases, agentic AI patterns, tool integration, evaluation, guardrails, and LLM observability.\nExperience with MLflow, Databricks, Amazon SageMaker AI, or equivalent platforms for experimentation, model management, and deployment.\nHands-on experience with Docker, Kubernetes or Amazon EKS, Terraform or AWS CloudFormation, and CI/CD using GitHub Actions, Jenkins, AWS CodePipeline, Azure DevOps, or equivalent tools.\nStrong understanding of distributed systems, microservices, event-driven architecture, security, identity and access management, networking, data protection, API management, and cost optimisation.\nWorking knowledge of Azure or other cloud platforms is beneficial but not mandatory.\n\nConsulting Experience: \nProven track record in an IT consulting environment, engaging with large enterprises and MNCs in strategic data and AI solutioning projects.\nStrong stakeholder management, business-needs assessment, solution shaping, and change-management skills.\n\nLeadership and Soft Skills: \nProven ability to lead, manage, coach, and develop high-performing engineering teams.\nAbility to operate as a hands-on player-coach who stays close to architecture, design, code quality, productionisation, and complex technical problem-solving while guiding the team.\nStrong stakeholder management, executive communication, and the ability to translate complex AI topics into clear business implications.\nExperience driving engineering strategy, delivery governance, prioritisation, and organisational capability development.\nDemonstrated ability to influence decisions and align cross-functional teams without relying solely on formal authority.\nStrong problem-solving, decision-making, coaching, conflict-resolution, and team-building skills.\nExcellent written and verbal communication, presentation, facilitation, and mentoring capabilities.\n\nPreferred Qualifications: \nBachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related field.\nAWS certification such as AWS Certified Machine Learning Engineer - Associate, AWS Certified Solutions Architect - Professional, or a relevant AWS speciality or professional-level certification.\nExperience managing AI engineering teams in an enterprise product, shared-services, consulting, or global capability-centre environment.\nExperience establishing AI engineering standards, reusable accelerators, technical communities of practice, or platform adoption patterns.\nExperience with AI governance, responsible AI, security reviews, model risk, data privacy, and production-readiness assessments.\n\nGood to Have: \nTechnical leaders who enjoy building both systems and teams.\nPlayer-coaches who can provide architectural guidance while enabling engineers to grow and succeed.\nStrategic thinkers who connect business outcomes with practical AI technology investments.\nPeople leaders who foster collaboration, innovation, accountability, inclusion, and continuous improvement.\nIndividuals who translate complex AI concepts into secure, scalable solutions and measurable value.\nAdaptable leaders who stay current with the rapidly evolving AI landscape while maintaining engineering discipline.\nSkills\nAWS, Bedrock, Databricks, Docker, EKS, LLMOps, Lambda, MLOps, MLflow, Python, SageMaker","description_format":"text","description_chars":8274,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Commerce","Energy 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