Provectus

Senior AI/ML Engineer (GenAI, AWS)

Ukraine

Live, first seen by us 5 days ago

Pay: Pay not listed
Work setting: Remote — work setting source
Work setup in the employer’s posting: remote
From the employer’s posting
Employment: Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting

What you’ll work on

Full posting

Build and ship production GenAI and agentic systems into client environments
Build and optimize RAG systems for production use cases
Build the evaluation harness before building the feature
Take systems to production on AWS, containerised, with CI/CD
Mentor junior and mid-level AI engineers and conduct code reviews

From the employer’s posting

Build and ship production GenAI systems into the customer’s environment

Build and optimize RAG systems for production use cases

Build the evaluation harness before you build the feature.

Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD.

Mentor junior and mid-level AI engineers, conduct code reviews

What you’ll bring

All qualifications

Core experience

Excellent communication and problem-solving skills
5+ years in software or ML engineering, with production systems you were accountable for.
Experience in one of the industries: financial services, insurance, healthcare.
Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.
Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.
Experience in an additional language (Go, TypeScript, or Rust).

Qualification wording

Excellent communication and problem-solving skills

5+ years in software or ML engineering, with production systems you were accountable for.

Experience in one of the industries: financial services, insurance, healthcare.

Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.

Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.

Experience in an additional language (Go, TypeScript, or Rust).

Benefits in the posting

Full benefits wording

Remote-friendly culture
Access to the latest AI tools and premium subscriptions
Private medical insurance or a budget for your medical needs
Paid sick leave, vacation, and public holidays
How we hire:
Employment type
Full-time

From the employer’s posting.

Apply at Provectus

Posting record

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Status in our records: Live
First seen by us: Oct 30, 20265 days ago
Recorded sightings: 6
Last seen by us: Nov 4, 2026
Reposted: once since Aug 23, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.

How this job compares

Time since we first saw it

Day 5

Early. Similar jobs usually come down between day 6 and day 69.

From 4,347 similar postings that came down. Coming down is not always a hire.

Provectus is hiring

5 open jobs

Open jobs by the week we first saw them, last 12 weeks.

ML Engineer hiring is picking up
Takes jobs down at a usual pace
60% of open jobs were reposted

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
Rust
TypeScript
AWS
Azure
GCP
Kubernetes
S3
Spark
Airflow
Go

Where the posting names them

- Experience building and optimizing RAG systems in production. - Strong 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. - Hands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus.

- Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines. - Experience in an additional language (Go, TypeScript, or Rust). - Experience with Apache Spark, Apache Airflow, Kafkа

- 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. - Our 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.

- Our 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. - Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements. Where this role sits

- Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure - Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks. - Experience building and optimizing RAG systems in production.

- Strong 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. - Hands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus. - Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines.

- Consulting, professional services, or other embedded customer-facing delivery. - AWS and Claude Code Certifications - A2A: you can explain agent-to-agent interoperability

- Integrate AI components into backend services and RESTful APIs - Take 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 - Start 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

- Remote-friendly culture - Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance - Career growth; we actively develop our engineers

- Hands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus. - Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines. - You 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.

- Experience in an additional language (Go, TypeScript, or Rust). - Experience with Apache Spark, Apache Airflow, Kafkа Responsibilities:

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Has Provectus posted this job before?

Yes. We’ve followed 6 other Senior AI/ML Engineer (GenAI, AWS) jobs there since May 2026. 3 came down, usually after about 68 days. 3 are still up.

MedellínCame down after 84 days, first seen May 25
Place not statedStill up after 160 days, first seen May 28
Place not statedStill up after 106 days, first seen Jul 21
Place not statedCame down after 68 days, first seen Aug 23
North MacedoniaCame down after 68 days, first seen Aug 23
North MacedoniaStill up after 5 days, first seen Oct 30
This jobUp 5 days so far, first seen Oct 30

If this one keeps the same pace, it may come down around January 6.

Large employers often post one title for several teams, so these can be separate openings.

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025: $140,300
Expected change, 2025 to 2035: 21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

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.
Our 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.
Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements.

Where this role sits

You will work in a small, senior pod alongside an FDE and an FDX,
- Forward 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.
- Forward 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.
- Senior 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.

Requirements:

Mindset

Proactive and self-directed; you push for clarity rather than waiting for a ticket
Excellent communication and problem-solving skills
Comfort with ambiguity and ownership.
B2+ English, comfortable collaborating across distributed, multicultural teams.

Technical depth

5+ years in software or ML engineering, with production systems you were accountable for.
Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes.
Shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks.
Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure
Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.
Experience building and optimizing RAG systems in production.
Strong 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.
Hands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus.
Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines.
You 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.
Model and agent monitoring, drift detection.
Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs.
Hands-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.
MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus.

Nice to Have:

Experience in one of the industries: financial services, insurance, healthcare.
Consulting, professional services, or other embedded customer-facing delivery.
AWS and Claude Code Certifications
A2A: you can explain agent-to-agent interoperability
CI/CD pipeline experience (GitHub Actions, GitLab CI)
Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.
Experience in an additional language (Go, TypeScript, or Rust).
Experience with Apache Spark, Apache Airflow, Kafkа

Responsibilities:

Work in a pair with an FDE and an FDX.
Build and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions).
Build and optimize RAG systems for production use cases
Build the evaluation harness before you build the feature.
Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer.
Integrate AI components into backend services and RESTful APIs
Take 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
Start 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
Participate in technical discussions and architectural decisions
Conduct model evaluation, improve failure modes you find, optimize model performance, efficiency, and reliability
Mentor junior and mid-level AI engineers, conduct code reviews and share knowledge across the team through documentation, presentations, and workshops.

What We Offer:

The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment

A forward-deployed model working in small, senior teams alongside FDE and FDX

A growing AI delivery practice where you help build the tooling and frameworks, not just use them

Remote-friendly culture

Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance

Career growth; we actively develop our engineers

Access to the latest AI tools and premium subscriptions

Long-term B2B collaboration

Private medical insurance or a budget for your medical needs

Paid sick leave, vacation, and public holidays

Equipment and all the tech you need for comfortable, productive work

How we hire:

Intro conversation. The role, your background and aspirations, tech questions.
Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant
HR Interview. Soft skills and expectations
HM interview. Tech questions; a live engineering session is also possible

Employment type

Full-time

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