{"id":2604411,"url":"https://alion.io/job/qad-principal-ai-engineer-enterprise-transformation","title":"Principal AI Engineer - Enterprise Transformation","company":{"id":6493,"name":"QAD","domain":"qad.com","url":"https://alion.io/company/qad","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":31,"computed_at":"2026-11-05T05:54:30Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":61000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":47},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Strands Agents","optional":false},{"name":"Braintrust","optional":false},{"name":"CI/CD","optional":false},{"name":"Context Engineering","optional":false},{"name":"CrewAI","optional":false},{"name":"DeepEval","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Feature Store","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"Google ADK","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"IAM","optional":false},{"name":"Java","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Model Distillation","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Ragas","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Terraform","optional":false},{"name":"TypeScript","optional":false},{"name":"Vertex AI","optional":false},{"name":"Weaviate","optional":false},{"name":"LangChain","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-10-15T07:40:21Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-11-05T17:25:37Z","board_verified":true,"closed_at":null,"days_open":21,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":21},"description":"QAD | Redzone is building an enterprise AI platform to support AI-enabled and agentic workflows across the organisation. The AI Centre of Excellence (CoE) is accountable for turning that ambition into deployed, measured, production workflows across priority business functions.\nThe CoE is small, senior and high-leverage. It sets architecture and standards, builds reusable capability, and delivers through a combination of internal engineering and external implementation partners. This is one of six foundational hires and the first dedicated engineering hire in the team.\n The Senior AI Engineer is the primary builder in the CoE. You will take prioritised workflows from architecture and PRD through to working, evaluated, production-grade AI systems - and, just as importantly, factor what you build into a reusable component library so that the fifth workflow costs a fraction of the first.\nThis is an applied engineering role with unusually high leverage. You are not building one product; you are building the components, patterns and reference implementations that internal engineers and external partner pods will use to build many. The work spans retrieval, agent orchestration, evaluation, and the unglamorous production engineering - latency, cost, failure handling, observability - that separates a demo from a system the business can depend on.\nWhere this role sits\nReports to: Head of the AI Centre of Excellence\nTechnical direction from: Lead AI Architect - you will work alongside them daily, and you are expected to push back when a design will not survive contact with production\nWorks with: The functional AI Engagement Specialists who own the workflows, PRDs and quality bar for the business functions in scope\nAlso partners with QAD engineering teams, Data & Platform Engineering, Security & GRC, and external system integrators\nScope: Foundational engineering hire. Expect to set the engineering standard, mentor subsequent hires, and review partner-delivered code.\nKey responsibilities\nBuilding AI and agentic workflows\nBuild production AI and agentic workflows end to end - from PRD and architecture through implementation, evaluation, release and iteration in production.\n\nDesign and implement agent orchestration: multi-step flows, tool and API invocation, planning and routing, state and memory, retries, fallbacks, timeouts and human-in-the-loop checkpoints.\n\nMake the judgement call on deterministic control flow versus model-driven control flow - and default to the former wherever it produces the same outcome more reliably and more cheaply.\n\nIntegrate with enterprise systems - ERP, CRM, support, services delivery, knowledge and data platforms - including systems with imperfect APIs and imperfect data.\n\nRetrieval and context engineering\nOwn the retrieval stack: corpus onboarding, document parsing, chunking strategy, embedding selection, hybrid lexical and vector search, reranking, metadata filtering and query transformation.\n\nImplement permission-aware retrieval so that a user or an agent can only ever retrieve what that identity is entitled to see - non-negotiable in an enterprise context.\n\nHandle freshness and incremental indexing so that retrieval reflects the state of the business rather than a snapshot from onboarding day.\n\nMeasure retrieval quality explicitly and treat it as a tunable subsystem with its own metrics, not as an assumption.\n\nModel selection, tuning and evaluation\nSelect, tune and route models across tiers based on measured quality, latency and cost - not on reputation or recency.\n\nOwn prompt engineering, versioning, structured output and context strategy as versioned, tested artefacts under source control.\n\nApply fine-tuning, adapters or distillation only where evidence shows the return justifies the operating burden - and be able to make that argument either way.\n\nBuild the evaluation harness: golden datasets, offline evaluation, LLM-as-judge with human calibration, regression suites running in CI, and online quality monitoring with structured feedback capture.\n\nWork with the Engagement Specialists to turn a business quality bar into measurable criteria - and be honest when a workflow does not clear it.\n\nThe reusable component library\nDesign, build and maintain the shared component library - SDKs, shared services, templates, reference implementations and documentation - that both internal engineers and partner pods build against.\n\nTreat internal engineers and SI teams as customers of your library: versioning, backwards compatibility, examples, and documentation good enough that people use it without asking you.\n\nReview partner-delivered code and designs for conformance, quality and maintainability.\n\nProduction engineering and operations\nMeet explicit latency and cost budgets per workflow, using caching, batching, streaming, model tiering and prompt efficiency.\n\nBuild for graceful degradation: provider outages, rate limits, backpressure, partial failures and safe fallbacks.\n\nImplement observability for non-deterministic systems - full step-level tracing, token and cost telemetry, quality dashboards, and a triage path for incidents where nothing crashed but the output was wrong.\n\nOwn infrastructure as code, CI/CD, environment promotion, secret and credential handling, and testing discipline for everything the CoE ships.\n\nParticipate in the operational support model for live AI workflows, including post-incident review and remediation.\n\nCraft and team\nSet the engineering standard for the CoE and raise it as the team grows.\n\nMentor subsequent engineering hires and partner engineers.\n\nDocument decisions and trade-offs so that the next engineer inherits reasoning, not just code.\n\n What we are looking for\nEssential\n6+ years of professional software engineering, including 2+ years building and operating production LLM or GenAI systems - systems with real users and real operating cost, not notebooks or proofs of concept.\n\nExcellent Python, plus working competence in at least one other language (TypeScript, Go, Java or similar).\n\nGenuine production retrieval experience - you have built a RAG or hybrid search system, measured it, found it wanting, and improved it.\n\nAgent orchestration experience with frameworks such as LangGraph, LlamaIndex, Semantic Kernel, Google ADK, Strands, CrewAI or equivalent - together with the judgement to know when a framework is the wrong answer.\n\nStrong AWS and/or GCP experience, including managed AI services (Bedrock, SageMaker, Vertex AI or equivalent) alongside core compute, serverless, networking, IAM and managed data services.\n\nAPI design and enterprise integration, including authentication and authorisation patterns - OAuth 2.0 / OIDC, service-to-service authentication, and delegated or on-behalf-of access.\n\nContainerisation, infrastructure as code (Terraform, CDK or equivalent), CI/CD and real testing discipline.\n\nEvaluation rigour - you can define what “good” means numerically for a subjective task, defend the definition, and act on the result.\n\nCost and latency awareness as a design instinct, not an afterthought raised by finance.\n\nClear written communication - design notes, documentation and honest status.\n\nComfort with ambiguity and shifting priorities in an early-stage function.\n\nStrongly preferred\nVector and hybrid search infrastructure at production scale - OpenSearch, pgvector, Vertex AI Search, Elasticsearch, Pinecone, Weaviate or similar.\n\nEvaluation and observability tooling - LangSmith, Langfuse, Phoenix, Braintrust, Ragas, DeepEval or equivalent.\n\nMulti-tenant SaaS engineering, and the data isolation discipline that comes with it.\n\nERP, manufacturing or supply chain data experience.\n\nClassical ML and MLOps background - feature stores, model registries, monitoring and drift.\n\nFine-tuning, parameter-efficient adaptation, or model serving and optimisation.\n\nExperience building internal platforms or SDKs consumed by other engineers.\n\nOpen-source contribution in the AI engineering ecosystem.\n\nThe technical environment\nA predominantly AWS estate with a meaningful GCP footprint, multi-tenant SaaS products, ERP and manufacturing data, and a heterogeneous set of enterprise systems across CRM, support, services delivery, marketing and data platforms. Engineering capacity is a mix of internal team and external implementation partners.\nThe AI toolchain is not fixed. You will help select it - and then live with the consequences, which is the correct incentive.\nWhat this role is not\nNot a prompt engineering role. Prompting is one component of a system that also has retrieval, orchestration, evaluation, infrastructure and an operating cost.\n\nNot a research scientist role. We are not training foundation models. We are shipping reliable systems on top of them.\n\nNot a specification-writing role. You will build, and you will read partner code line by line.\n\nNot a role where quality is somebody else’s problem. If it goes to production, you own how well it works.\n\nWhat success looks like\nFirst 3 months\nThe first prioritised workflow in production or at production readiness, with a working evaluation harness behind it.\n\nBaseline engineering standards operating - repository structure, CI/CD, environments, tracing, secret handling.\n\nA shared understanding with the architect and the Engagement Specialists of what “good” means, expressed as numbers.\n\nFirst 6 months\nThe first version of the reusable component library in use - by you, by internal engineers, and by at least one partner pod.\n\nMultiple workflows live, with quality, latency and cost per transaction instrumented and visible.\n\nRegression evaluation running in CI, so a change that degrades quality fails before release rather than in front of a customer.\n\nFirst 12 months\nDemonstrably lower build cost and cycle time for each successive workflow, attributable to reuse.\n\nA production estate that is operable by the team - monitored, documented, supported, and not dependent on any one person.\n\nPartner-delivered code that meets the same standard as internal code, because the standard and the review are real.\n\nHow we assess\nTechnical conversation on a production AI system you built - architecture, evaluation approach, what broke, what you would do differently.\n\nPractical exercise in retrieval or agent orchestration, discussed rather than graded in isolation.\n\nCode and design review - you review something of ours, we review something of yours.\n\nCollaboration interview with the CoE and a functional stakeholder.\n\n Your health and well being are important to us at QAD. We provide programs that help you strike a healthy work-life balance.\nOpportunity to join a growing business, launching into its next phase of expansion and transformation.\nCollaborative culture of smart and hard-working people who support one another to get the job done.\nAn atmosphere of growth and opportunity, where idea-sharing is always prioritized over level or hierarchy.\nCompensation packages based on experience and desired skill set\nAbout QAD:\nQAD | Redzone is redefining manufacturing and supply chains through its intelligent, adaptive platform that connects people, processes, and data into a single System of Action. With three core pillars - Redzone (frontline empowerment), Adaptive Applications (the intelligent backbone), and Champion AI (Agentic AI for manufacturing) - QAD | Redzone helps manufacturers operate with Champion Pace, achieving measurable productivity, resilience, and growth in just 90 days.\nQAD is committed to ensuring that every employee feels they work in an environment that values their contributions, respects their unique perspectives and provides opportunities for growth regardless of background. 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