{"id":108950,"url":"https://alion.io/job/provectus-senior-aiml-engineer-genai-aws-2","title":"Senior AI/ML Engineer (GenAI, AWS)","company":{"id":2374,"name":"Provectus","domain":"provectus.com","url":"https://alion.io/company/provectus","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Bosnia and Herzegovina","Bulgaria","Croatia","Czech Republic","Montenegro","Moldova","North Macedonia"],"countries":["BA","BG","HR","CZ","ME","MD","MK"],"hiring_countries":["BA","BG","HR","CZ","ME","MD","MK"],"hiring_countries_total":7,"salary":null,"salary_estimate":{"min_usd":47000,"max_usd":122000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2365},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Azure","optional":false},{"name":"Claude","optional":false},{"name":"Claude Code","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"TypeScript","optional":false},{"name":"A2A","optional":true},{"name":"AgentOps","optional":true},{"name":"Airflow","optional":true},{"name":"CI/CD","optional":true},{"name":"GitHub Actions","optional":true},{"name":"GitLab CI","optional":true},{"name":"Go","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM Evaluation","optional":true},{"name":"LLMOps","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"NLP","optional":true},{"name":"Rest API","optional":true},{"name":"Rust","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-08-21T10:12:25Z","employer_posted_date":"2026-08-21","last_verified_at":"2026-10-30T08:11:40Z","board_verified":true,"closed_at":null,"days_open":70,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":70},"description":"Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes - through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide.\n\nOur work centers on two verticals - Financial Services & Insurance and Healthcare & Life Sciences - where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture.\n\nOur team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements.\n\nWhere this role sits\nYou will work in a small, senior pod alongside an FDE and an FDX,\nForward Deployed Executives (FDX) own the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs.\n\nForward Deployed Engineers (FDE) embed with a client, map the client workflow, identify the business problem underneath it, design and build a working AI solution, present to the client, and transfer the knowledge to the client's team. Owns technical direction of the whole solution.\n\nSenior AI Engineer. When an FDE comes back from the client with the business problem, you will help turn that into an agentic system that runs in production and will be responsible for evaluation, observability, and guardrails. You'll have real ownership of components and of the technical decisions inside them.\n\nRequirements:\nMindset\nProactive and self-directed; you push for clarity rather than waiting for a ticket\n\nExcellent communication and problem-solving skills\n\nComfort with ambiguity and ownership.\n\nB2+ English, comfortable collaborating across distributed, multicultural teams.\n\nTechnical depth\n5+ years in software or ML engineering, with production systems you were accountable for.\n\nSolid AI/ML foundations. You understand what the models do well enough to reason about failure modes.\n\nShipped to production LLM applications and agentic workflows - not demos, not POCs, not notebooks.\n\nAgentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure\n\nExperience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.\n\nExperience building and optimizing RAG systems in production.\n\nStrong engineering fundamentals. Full-stack mindset, comfortable across AI, backend development, and cloud infrastructure. Python and/or TypeScript proficiency; depth matters more than stack. Dropped into an unfamiliar codebase, you're productive.\n\nHands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus.\n\nCloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines.\n\nYou evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release.\n\nModel and agent monitoring, drift detection.\n\nCost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs.\n\nHands-on production experience with the Claude ecosystem - Claude Code, CLAUDE.md, hooks, skills files. Spec-driven development - writing the intent, constraints, and acceptance criteria before you let an agent build - is a strong plus.\n\nMCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus.\n\nNice to Have:\nExperience in one of the industries: financial services, insurance, healthcare.\n\nConsulting, professional services, or other embedded customer-facing delivery.\n\nAWS and Claude Code Certifications\n\nA2A: you can explain agent-to-agent interoperability\n\nCI/CD pipeline experience (GitHub Actions, GitLab CI)\n\nPractical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.\n\nExperience in an additional language (Go, TypeScript, or Rust).\n\nExperience with Apache Spark, Apache Airflow, Kafkа\n\nResponsibilities:\nWork in a pair with an FDE and an FDX.\n\nBuild and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions).\n\nBuild and optimize RAG systems for production use cases\n\nBuild the evaluation harness before you build the feature.\n\nWrite production code across the stack - AI, backend services, data pipelines. We choose tools to fit the customer.\n\nIntegrate AI components into backend services and RESTful APIs\n\nTake systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD. Implement LLMOps and AgentOps practices: agent tracing, prompt and version management, cost and latency monitoring, regression testing, drift detection\n\nStart from the blueprint, contribute to enablement and handover: clear documentation, runbooks, and pairing with the client engineers who will inherit the system. Feed reusable components and lessons back into the Provectus Blueprints\n\nParticipate in technical discussions and architectural decisions\n\nConduct model evaluation, improve failure modes you find, optimize model performance, efficiency, and reliability\n\nMentor junior and mid-level AI engineers, conduct code reviews and share knowledge across the team through documentation, presentations, and workshops.\n\nWhat We Offer:\nThe chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment\n\nA forward-deployed model working in small, senior teams alongside FDE and FDX\n\nA growing AI delivery practice where you help build the tooling and frameworks, not just use them\n\nRemote-friendly culture\n\nInternal training programs with full support for Claude, AWS, and other professional certifications, conference attendance\n\nCareer growth; we actively develop our engineers\n\nAccess to the latest AI tools and premium subscriptions\n\nLong-term B2B collaboration\n\nPrivate medical insurance or a budget for your medical needs\n\nPaid sick leave, vacation, and public holidays\n\nEquipment and all the tech you need for comfortable, productive work\n\nHow we hire:\nIntro conversation. The role, your background and aspirations, tech questions.\n\nTechnical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant\n\nHR Interview. Soft skills and expectations\n\nHM interview. 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