{"id":3074476,"url":"https://alion.io/job/charter-global-senior-manager-ai-engineering-delivery","title":"Senior Manager - AI Engineering & Delivery","company":{"id":693413,"name":"Charter Global","domain":"charterglobal.com","url":"https://alion.io/company/charterglobal","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":44000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":38},"experience_years_min":15,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"DLP","optional":false},{"name":"Docker","optional":false},{"name":"Function Calling","optional":false},{"name":"GitHub","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Informatica","optional":false},{"name":"Interpretability","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"MLFlow","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenSearch","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":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Time Series","optional":false}],"status":"live","first_seen_at":"2026-10-22T10:01:12Z","employer_posted_date":null,"last_verified_at":"2026-10-22T10:01:12Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"About client:\n\nGlobal AI & Innovation team to build next-generation AI platforms, autonomous agents, and real-time intelligence solutions that transform how a global enterprise operates and serves its customers.\n\nPosition Summary:\n\nIt's seeking a highly experienced, hands-on Senior Manager, AI Engineering & Delivery to lead the Hyderabad GCC AI delivery organization and serve as a critical execution partner to the Global AI & Innovation leadership team.\n\nThis role will own the design, delivery, operationalization, and support of enterprise AI, machine learning, Generative AI, and Agentic AI solutions across the prioritized global pipeline.\n\nThe successful candidate will manage a multidisciplinary team of AI/ML Engineers, Agent Developers, Data Scientists, MLOps resources, contractors, and delivery partners.\n\nThe role will act as the single point of contact for the onshore AI team and business stakeholders, translate operational needs into scalable technical solutions, lead Responsible AI training and awareness, and serve as the first-level escalation point for AI/ML and Agentic AI issues.\n\nThis is a strategic leadership role requiring deep knowledge of business operations, strong stakeholder management, and the technical depth to design and deploy secure, reusable, production-grade AI capabilities that integrate into business workflows and create measurable value.\n\nKey Responsibilities:\n\nGlobal AI/ML & Agentic Solution Delivery:\n\n- Lead end-to-end delivery of AI, ML, Generative AI, and Agentic AI initiatives across the prioritized global portfolio, from discovery and design through production deployment, adoption, and value realization.\n\n- Create delivery plans, estimates, milestones, resource models, technical dependencies, risk controls, and release readiness criteria.\n\n- Ensure solutions are scalable, reusable, maintainable, observable, secure, and aligned with enterprise architecture and engineering standards.\n\n- Establish reusable implementation patterns, accelerators, components, APIs, and reference architectures to improve speed and quality across the portfolio.\n\n- Maintain transparent portfolio reporting on scope, schedule, quality, risks, dependencies, budget, adoption, and business outcomes.\n\nAI Architecture & Hands-on Technical Leadership:\n\n- Own solution architecture and technical design for AI/ML applications, AI microservices, RAG solutions, intelligent assistants, and single- and multi-agent workflows.\n\n- Guide model, algorithm, platform, orchestration, data, integration, and deployment decisions based on business requirements and operational constraints.\n\n- Conduct architecture and code reviews; establish engineering standards for testing, evaluation, observability, resiliency, security, and supportability.\n\n- Remain hands-on in design, prototyping, troubleshooting, performance optimization, and production deployment of critical solutions.\n\n- Design APIs and integrations that embed AI capabilities into operational processes, enterprise applications, and business workflows.\n\nGCC Team Leadership & Delivery Management:\n\n- Build, lead, coach, and develop a high-performing multidisciplinary GCC team of AI/ML Engineers, Agent Developers, Data Scientists, MLOps Engineers, contractors, and partner resources.\n\n- Allocate work across the portfolio, balance capacity and demand, remove delivery blockers, and maintain accountability for quality and outcomes.\n\n- Define role expectations, engineering practices, onboarding plans, technical learning paths, and knowledge-sharing mechanisms.\n\n- Manage contractors and vendors against defined deliverables, acceptance criteria, security requirements, and performance expectations.\n\n- Partner with onshore leadership to evolve the global AI operating model and create effective follow-the-sun delivery and support practices.\n\nBusiness Partnership & Operational Knowledge:\n\n- Serve as the primary GCC AI point of contact for the onshore team, technology leaders, and business stakeholders.\n\n- Develop detailed knowledge of It's business operations, workflows, data, controls, customer needs, and regional differences.\n\n- Facilitate discovery workshops and translate operational pain points into well-defined AI use cases, solution options, and delivery roadmaps.\n\n- Partner with business owners to define success measures, adoption plans, process changes, controls, and quantified value outcomes.\n\n- Communicate complex technical concepts clearly to executives, business leaders, product owners, engineers, and control functions.\n\nAI Governance, Security & Responsible AI:\n\n- Operationalize It's AI governance requirements throughout the solution lifecycle, including intake, design, risk assessment, approval, testing, deployment, monitoring, and retirement.\n\n- Ensure security-by-design and privacy-by-design for sensitive financial, customer, employee, and operational data.\n\n- Implement safeguards for prompt injection, data leakage, hallucination and inaccuracy, unauthorized access, model misuse, bias, explainability, and regulatory or compliance risk.\n\n- Maintain solution documentation, decision records, model cards, evaluation evidence, approvals, risk treatments, and audit ready artifacts.\n\n- Partner with Information Security, Privacy, Legal, Compliance, Enterprise Architecture, Data Governance, and Internal Audit as required.\n\nGlobal AI Training, Adoption & Awareness:\n\n- Own and drive continuous AI learning, enablement, and Responsible AI awareness across global It's teams.\n\n- Develop and deliver executive briefings, business enablement sessions, technical workshops, developer training, and role based learning paths.\n\n- Create practical guidance that helps employees use AI safely, responsibly, and effectively within approved policies and tools.\n\n- Build a network of AI champions and reusable training assets to scale adoption across regions and functions.\n\n- Track participation, adoption, proficiency, policy awareness, and feedback to continuously improve enablement programs.\n\nAI Operations, Support & Escalation:\n\n- Serve as the first-level escalation point for delivery, production, model, agent, data, integration, governance, and adoption issues related to AI/ML and Agentic AI.\n\n- Lead structured triage, root-cause analysis, stakeholder communication, remediation, and preventive action for critical incidents.\n\n- Establish operational readiness standards, service ownership, monitoring, alerting, runbooks, support handoffs, and incident response procedures.\n\n- Monitor model and agent quality, latency, reliability, cost, drift, safety, and business performance after deployment.\n\n- Escalate material risks and decisions promptly with clear impact, options, recommendations, owners, and resolution plans.\n\nStrategy Execution & Value Realization:\n\n- Reporting directly to Global Head, AI & Innovation to translate the Global AI Strategy into an executable roadmap and delivery portfolio.\n\n- Support portfolio prioritization, investment planning, governance forums, executive reviews, vendor evaluations, and strategic partnerships.\n\n- Identify opportunities to reuse platforms, data products, models, agents, and integration patterns across functions and regions.\n\n- Define and report measurable outcomes, including business value, adoption, risk reduction, cycle-time improvement, quality, reliability, and cost efficiency.\n\n- Continuously evaluate emerging technologies and recommend practical adoption paths aligned with business need, enterprise readiness, and risk tolerance.\n\nRequired Qualifications:\n\n- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Information Systems, or a related discipline.\n\n- 12+ years of progressive experience across software engineering, data science, AI/ML engineering, enterprise architecture, or digital solution delivery.\n\n- 5+ years leading multidisciplinary AI/ML, data science, or advanced engineering teams, including contractors and delivery partners.\n\n- Demonstrated success designing, deploying, and supporting production-grade enterprise AI/ML or Generative AI solutions.\n\n- Experience operating as a technical and delivery leader across geographically distributed onshore and offshore teams.\n\n- Strong executive communication, stakeholder management, prioritization, risk management, and delivery governance skills.\n\n- Ability to combine strategic leadership with hands-on technical problem solving and detailed operational understanding.\n\nRequired Technical Skills & Experience:\n\nAI, Generative AI & Agentic Engineering:\n\n- LLMs, prompt engineering, structured outputs, tool use, function calling, AI agents, multi-agent orchestration, RAG, embeddings, semantic search, model evaluation, grounding, guardrails, and human-in-the-loop patterns.\n\n- Hands-on experience with Azure OpenAI and/or Amazon Bedrock and with relevant model providers and enterprise AI services.\n\n- Experience with frameworks such as Semantic Kernel, LangChain, LangGraph, AutoGen, or comparable orchestration frameworks.\n\nData Science & Algorithm Selection:\n\n- Strong foundation in statistics, experimentation, feature engineering, predictive modeling, optimization, and model evaluation.\n\n- Ability to select algorithms based on business objective, target variable, data volume and quality, latency, interpretability, risk, cost, and deployment constraints.\n\n- Practical experience with regression, classification, decision trees, random forests, gradient boosting, clustering, anomaly detection, time-series forecasting, NLP, recommendation methods, neural networks, and optimization techniques.\n\n- Ability to compare candidate models using appropriate evaluation metrics, validation strategies, explainability methods, and business acceptance criteria.\n\nSoftware Engineering & Integration:\n\n- Advanced proficiency in Python and SQL; strong understanding of APIs, microservices, event-driven integration, object-oriented design, automated testing, version control, and secure coding.\n\n- Experience with REST services, containers, Docker, Kubernetes, GitHub or Azure DevOps, CI/CD, infrastructure as code, and modern DevSecOps practices.\n\nData Engineering & Knowledge Platforms:\n\n- Experience with batch and real-time data ingestion, transformation, orchestration, streaming, operational data, document processing, metadata, and data quality.\n\n- Experience with Snowflake cortex, Microsoft Fabric, Azure Data Factory, Azure Synapse, Informatica Claire, Apache Spark, Kafka or comparable technologies.\n\n- Experience designing vector search and knowledge retrieval using Azure AI Search, OpenSearch, or comparable vector database technologies.\n\nCloud, MLOps & Observability:\n\n- Strong experience designing and operating solutions across Azure and AWS.\n\n- Experience with model and agent deployment, registries, experiment tracking, automated testing, monitoring, drift detection, telemetry, performance tuning, autoscaling, cost optimization, secrets management, and production support.\n\n- Familiarity with MLflow and relevant cloud-native AI/ML services.\n\nAI Security, Governance & Responsible AI:\n\n- Knowledge of secure AI design, identity and access management, encryption, secrets management, DLP, privacy, retention, auditability, model risk, bias and fairness, explainability, and regulatory compliance.\n\n- Experience implementing controls for prompt injection, insecure tool use, sensitive-data exposure, hallucination, unsafe outputs, unauthorized actions, model misuse, and third-party AI risk.\n\nBusiness & Domain Experience:\n\n- Strong knowledge of business operations at a detailed process level is essential.\n\n- Experience in one or more of the following areas is highly desirable:\n\n1. Secure logistics and cash management\n\n2. Financial services, banking, payments, or regulated operations\n\n3. Route, branch, vault, fleet, or field-service operations\n\n4. Customer service, sales, pricing, revenue, and retention analytics\n\n5. 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