Forward Deployed Engineer

About the Role:

We are looking for a Forward Deployed Engineer to work closely with customers and build technical solutions to solve real-world business problems.

This is a highly customer-facing engineering role, combining full-stack development, AI and solution engineering. You will work in ambiguous environments, take end-to-end ownership and turn customer requirements into working product solutions.

Key Responsibilities:

• Work directly with customers to understand technical and business requirements.
• Design, build and deploy full-stack solutions and product features.
• Integrate AI/LLM capabilities into applications and workflows.
• Troubleshoot technical challenges and develop practical solutions.
• Collaborate with product and engineering teams to deliver customer-focused solutions.
• Take ownership of projects from requirement gathering through implementation.
• Work effectively in fast-paced and ambiguous environments.

What We're Looking For:

• Experience as a Full Stack Engineer, Product Engineer, AI Engineer, Solutions Engineer, Solutions Architect or similar role.
• Strong understanding of both frontend and backend development.
• Experience building and contributing to real-world product features.
• Exposure to AI/LLMs, Copilots, AI automation or GenAI tools.
• Strong problem-solving and customer-facing communication skills.
• Ability to work independently and take ownership of outcomes.
• Comfortable working with evolving requirements and ambiguity.

Preferred Background

• Candidates from product/SaaS companies, AI startups, or modern technology environments are preferred.
• Experience working with product clients through a service-based organization will also be considered, provided you have strong hands-on product engineering experience.
• Important: This is not a backend-only role. Strong full-stack exposure and the ability to work directly with customers are essential.

Forward Deployed Engineer (FDE)

About the role

We are hiring a Forward Deployed Engineer (FDE) who takes a client's problem from the first conversation to a working product in production. You will build both the front end and the back end, use AI tools as a daily part of how you work, and own the outcome rather than a ticket queue.

You will also speak with our client directly: demos, status updates, scoping, and respectful pushback when a request will not serve their goal. You will work as part of a team, on site in our Bangalore office.

As a Forward Deployed Engineer, you stay close to the client's problem and outcome using an AI Native approach.

What you will own

You own the outcome from discovery to post-launch, not a single layer of the stack.

• Discover and scope: turn a vague client need into a clear problem statement, a success metric, and a small first release.

• Design and build: deliver the user interface, APIs, and database for features end to end, choosing the simplest approach that works.

• Ship and run: set up deployment (CI/CD, cloud basics), add monitoring, and fix production issues you or your team introduced.

• Communicate with clients: run demos and status calls, write clear async updates, give honest estimates, and flag risks early.

• Iterate on results: review usage and feedback after launch, and decide what to improve, cut, or leave alone.

• Work with the team: review code, share what works with AI tools, and help teammates unblock their own work.

• Protect quality and trust: write tests, review AI-generated code critically, and handle client data and secrets responsibly.

What "AI-native" means here

AI-native means AI tools are part of how you plan, build, and check your work, and you stay accountable for every line that ships. Familiarity with a tool name is not enough.

• You work with coding agents daily (for example Claude Code, Cursor, or Copilot) and can explain how you steer them: specs, context files, task breakdown, and when you take the keyboard back.

• You review AI output critically. You test it, read it for security and maintainability problems, and reject what does not hold up.

• You verify, not just generate. Tests, logging, and checks come with your work, because speed without confidence is not delivery.

• You know when not to use AI, and you can explain that choice to a client in plain language.What we are looking for Experience level matters less than how you work. We value agile, quick learners who can show real shipped work.

Technical baseline

• Language-agnostic engineering: you pick up new languages and frameworks quickly and choose the right tool for the problem, rather than defending a favorite stack.

• Real strength in a few languages or frameworks (for example JavaScript or TypeScript, Python, Java, Go, or C#), shown in projects that span both the front end and back end.

• Fundamentals that carry across stacks: web basics (HTML, CSS, HTTP), designing and consuming APIs, and data modeling with SQL databases.

• Git, pull requests, and basic CI/CD and cloud deployment.

• A track record of using AI tools in real projects.

How you work

• Communication: clear spoken and written English, comfortable on client calls, and able to explain technical trade-offs to non-technical people.

• Ownership: you follow a problem through to resolution and raise issues before they become surprises.

• Outcome focus: you measure your work by what changed for the user or the client, not by lines of code or tasks closed.

• Product thinking: you ask who the user is, what problem we are solving, and how we will know it worked. You suggest simpler options and say what you would leave out.

• Problem solving: you break down unclear problems, form hypotheses, and debug methodically instead of guessing.

• Agility: you are comfortable with changing requirements, short cycles, and learning new tools quickly.

Nice to have

• Prior client-facing, consulting, or agency experience, especially with North American clients.

• Docker, a major cloud provider (AWS, Azure, or GCP), and infrastructure basics.

• Experience with testing frameworks, observability tools, and performance tuning.

• Awareness of secure coding, privacy expectations, and common client compliance requirements

such as SOC 2.

• Open-source contributions, technical writing, or a deployed side project you can demo.

How we evaluate

We look at what you have built, not only how you interview. Please be ready to share your Git repositories and any deployed work.

1. Application and Git review: send a link to your repositories (GitHub, GitLab, or similar) with a short note on the project you are proudest of. We read your commit history, pull requests, tests, and README, and try the deployed demo if there is one.2. Project walkthrough: a conversation where you explain one project in depth: the problem, your

key decisions, what broke, what you would change, and how you used AI along the way.

3. Practical exercise with AI tools allowed: a short build task where we watch how you plan, direct your tools, and verify the result.

4. Client communication round: a mock client call where you present your work, answer questions, and handle a change in scope.

5. Team and values conversation: how you collaborate, take feedback, and own mistakes.

If your best work is under NDA, tell us. You can describe it in the walkthrough and show a side project in the repository review instead.

Role details

Title : Forward Deployed Engineer (FDE)

Employment type : Full-time employee

Location : On site, Bangalore office (not at client sites)

Experience level : Open: we hire for agility and proven work, not

years

Team :Part of a project team, working alongside a

product or account lead

Client contact : Shared: you and the lead both speak with North

American clients

Domain : Generalist, across industries

Working-hours overlap with North America and compensation range are still to be confirmed before this is published.

If any candidates interested for above role, pls drop message on this WhatsApp number

nine three one six one two zero one three two

Role Summary:

We are looking for a Forward Deployed Engineer with strong hands-on experience in Databricks and Generative AI/Claude to work closely with clients, business stakeholders, and internal engineering teams. The ideal candidate will combine strong Data Engineering, Software Engineering, Databricks, and Generative AI skills with the ability to understand business problems and rapidly build, deploy, and optimize production-ready solutions. This is a client-facing, hands-on engineering role where you will work from problem discovery and solution design through POC development, production deployment, and ongoing optimization.

Key Responsibilities:

Forward Deployed Engineering

• Work directly with clients and stakeholders to understand business and technical requirements.
• Translate business problems into scalable data, AI, and software solutions.
• Design and develop POCs and rapidly validate technical solutions.
• Convert successful POCs into reliable, production-ready applications.
• Work closely with client engineering and data teams during implementation and deployment.
• Troubleshoot production issues and continuously optimize deployed solutions.
• Act as a technical bridge between clients, delivery teams, data engineers, AI engineers, and architects.

Databricks & Data Engineering

• Design and develop scalable data solutions using Databricks, PySpark, Python, and SQL.
• Build and optimize data ingestion, transformation, and ETL/ELT pipelines.
• Work with Databricks Lakehouse, Delta Lake, and Unity Catalog.
• Develop Databricks Workflows and production data pipelines.
• Implement data processing solutions for structured and semi-structured datasets.
• Optimize Databricks workloads for performance, scalability, reliability, and cost.
• Integrate Databricks with databases, APIs, cloud platforms, and enterprise applications.

Generative AI & Claude

• Build enterprise AI solutions using Claude and other Large Language Models (LLMs).
• Integrate Claude APIs into applications and business workflows.
• Develop RAG (Retrieval-Augmented Generation) solutions using enterprise data.
• Work with embeddings, vector search, semantic search, and knowledge retrieval.
• Develop AI-powered applications for summarization, classification, information extraction, question answering, and document processing.
• Implement prompt engineering, structured outputs, tool/function calling, and context management.
• Develop and integrate AI agents and multi-step AI workflows where applicable.
• Evaluate LLM responses for accuracy, relevance, groundedness, latency, and cost.
• Implement appropriate AI guardrails, security, and data privacy controls.

Production & Deployment

• Deploy AI and data solutions into production environments.
• Work with APIs, microservices, Git, CI/CD, containers, and cloud platforms.
• Monitor application and pipeline performance and troubleshoot issues.
• Collaborate with Data Scientists and ML Engineers to productionize AI/ML models.
• Ensure solutions meet security, scalability, reliability, and maintainability requirements.

Required Skills & Experience

• 4+ years of experience in Data Engineering, Software Engineering, AI/ML Engineering, or a related field.
• Strong hands-on experience with Databricks.
• Strong proficiency in Python, PySpark, and SQL.
• Experience with Delta Lake and Lakehouse Architecture.
• Experience working with Generative AI / LLMs.
• Hands-on experience with Claude / Anthropic APIs is preferred.
• Experience with RAG, embeddings, vector databases, and semantic search.
• Strong understanding of REST APIs and enterprise integrations.
• Experience developing production-grade applications and data pipelines.
• Strong problem-solving and troubleshooting capabilities.
• Excellent communication and client-facing skills.

Preferred Skills

• Experience with Claude Code / Anthropic ecosystem.
• Experience with OpenAI, Azure OpenAI, AWS Bedrock, or other LLM platforms.
• Experience with LangChain, LangGraph, LlamaIndex, or equivalent frameworks.
• Experience with Databricks Unity Catalog, Workflows, and MLflow.
• Experience with AWS, Azure, or GCP.
• Experience with Docker, Kubernetes, and CI/CD.
• Exposure to AI agents and agentic workflows.
• Knowledge of AI evaluation, guardrails, security, and responsible AI.
• Experience working in consulting, client delivery, or customer-facing engineering environments.

Key Competencies

• Strong customer-facing and stakeholder management skills.
• Ability to understand ambiguous business problems and translate them into technical solutions.
• Strong ownership and execution mindset.
• Ability to rapidly prototype, iterate, and productionize solutions.
• Strong analytical and troubleshooting skills.
• Comfortable working in fast-paced and dynamic client environments.
• Excellent written and verbal communication.
• Ability to work independently as well as collaboratively with distributed teams.

Hiring: 10 openings for Associate Data Forward Deployed Engineer (0–2 yrs) for a fast-growing AI platforms & services company, Hyderabad (confidential client) via Ziro.digital.

Location: Hyderabad (onsite, full-time). Open to candidates from anywhere in India willing to relocate. Experience: 0–2 years. 2024/2025/2026 graduates welcome (B.Tech/B.E/MCA/M.Sc CS/IT or related).

What you'll do: build data pipelines end to end (ingest, transform, model, deliver), work with clients on their data, and use AI for query generation, data quality and debugging.

Must have: strong SQL (write, read, tune); Python for data; SQL Server and/or Postgres; at least one end-to-end data pipeline; data-analysis fundamentals; a GitHub with data projects.

Good to have: S3/Azure Blob, Airflow/mage.ai, DBT/Fabric, Snowflake/Databricks/BigQuery, GitLab CI/CD, Grafana, LLM/RAG awareness.

Compensation & terms: 6-month paid internship at ₹20,000/month, then ₹7 LPA CTC on conversion. Relocation to Hyderabad required.

Selection: Ziro assessment (Communication, Technical, AI interview, Coding), then the company's rounds (aptitude, AI screening, project build, in-office hackathon, technical panel, leadership).

Apply by Wed 21 Oct 2026.

Screening questions (please answer in your application):

1) Willing to relocate to Hyderabad and work onsite?

2) Do you accept a 6-month internship at ₹20k/month, then ₹7 LPA?

3) GitHub link + one project you built.

4) Graduation year, current city, notice period.

Job Overview

We are looking for an experienced AI/ML Engineer with strong full-stack development skills to design, develop, and deploy AI-powered applications. The ideal candidate should be proficient in Python backend development, frontend technologies, and LLM integration, with the ability to build end-to-end AI-native products, custom dashboards, and lightweight applications.

This role focuses on applying AI to real-world business problems through rapid prototyping, prompt engineering, Retrieval-Augmented Generation (RAG), and scalable application development.

Key Responsibilities

• Design, develop, and deploy end-to-end AI-powered applications.
• Build backend services and REST APIs using Python.
• Develop responsive frontends using React, Next.js, or similar frameworks.
• Integrate LLM APIs such as OpenAI, Claude, and Hugging Face into production applications.
• Build custom dashboards, internal tools, micro-apps, and AI-driven business solutions.
• Implement prompt engineering, embeddings, RAG pipelines, and context-aware AI systems.
• Rapidly prototype and validate ideas using modern AI-assisted development workflows.
• Optimize application performance, scalability, reliability, and AI usage costs.
• Work with data processing libraries such as Pandas and NumPy.
• Collaborate with product and business teams to translate requirements into functional AI solutions.
• Take ownership of features from ideation and development through testing, deployment, and production support.

Required Skills & Qualifications

• 2+ years of experience in software engineering, AI engineering, or application development.
• Strong proficiency in Python and backend development.
• Hands-on experience with React, Next.js, or similar frontend technologies.
• Practical experience integrating LLM APIs such as OpenAI, Claude, or Hugging Face.
• Understanding of REST APIs, microservices, and system design principles.
• Knowledge of prompt engineering, embeddings, and RAG architectures.
• Experience with data processing using Pandas and NumPy.
• Ability to independently build, test, and deploy full-stack applications.
• Strong problem-solving, debugging, and analytical skills.
• Ability to work in a fast-paced environment with a strong ownership mindset.

Good to Have

• Experience with LangChain, LlamaIndex, or similar AI frameworks.
• Familiarity with vector databases such as Pinecone, Weaviate, or Supabase.
• Experience building AI chat interfaces, copilots, assistants, and custom dashboards.
• Familiarity with cloud platforms, application deployment, and production monitoring.
• Experience with AI-assisted coding tools and rapid prototyping workflows.

What We Are Looking For

• A full-stack AI engineer who is comfortable working across both backend and frontend development.
• Someone focused on building practical AI-powered products rather than only training machine learning models.
• A developer who can prototype quickly, validate ideas, and iterate efficiently using a “vibe coding” approach.
• A self-driven professional who can take ownership of a feature from concept to production.

Please note that this job is only for Ahmedabad location.

About Us

We believe the future of software development is AI-native — where engineers operate at a higher level of abstraction and quality remains non-negotiable. 

Incubyte is a software craft consultancy where the “how” of building software matters as much as the “what”.  

We partner with companies of all sizes, from helping enterprises build, scale, and modernize to early-stage founders bring their ideas to life. 

Our engineers operate in an AI-native development model, using AI as a collaborator across the SDLC to accelerate development while upholding the discipline of software craftsmanship. Guided by Software Craftsmanship and Extreme Programming practices, we build reliable, maintainable, and scalable systems with speed, without compromising quality. If this way of building software resonates with you, we’d like to talk. 

Our Guiding Principles 

These principles define how we work at Incubyte. They are non-negotiable. 

Relentless Pursuit of Quality with Pragmatism 

  We build high-quality systems without losing sight of delivery. 

Extreme Ownership 

  We take responsibility end-to-end for decisions, execution, and outcomes. 

Proactive Collaboration 

  We collaborate closely, challenge each other, and solve problems together. 

Active Pursuit of Mastery 

  We continuously improve our craft and raise our bar. 

Invite, Give, and Act on Feedback 

We seek, give, and act on feedback to get better every day. 

Ensuring Client Success 

We act as trusted partners and focus on real outcomes, not just output. 

Experience Level

This role is ideal for engineers with total 3+ years of experience with a proven track record of shipping complex projects successfully.

An experienced individual contributor and leader who thrives in large, complex projects with widespread impact.

What You’ll Do as a Software Craftsperson 

• Design and build high-quality, maintainable systems using disciplined engineering practices such as TDD, continuous refactoring, and pair programming 
• Operate in an AI-native development model, using AI as a collaborator to explore architecture and design, accelerate development, and continuously improve systems while applying strong judgment to ensure that speed never compromises quality 
• Take end-to-end ownership of outcomes from problem understanding and system design to implementation, deployment, and operation in production 
• Make thoughtful design decisions that balance simplicity, scalability, and long-term maintainability in real-world systems 
• Maintain a high bar for engineering quality through rigorous testing, code reviews, and continuous feedback 
• Investigate and resolve production issues, and implement systemic improvements to prevent recurrence 
• Work directly with clients, navigate ambiguity, and translate business problems into well-designed technical solutions 
• Contribute to improving team practices, tooling, and systems to raise the overall quality and effectiveness of engineering 

Requirements

What You’ll Bring 

• 3+ years of experience building high-quality, production systems (flexible based on demonstrated capability) 
• Strong fundamentals in software engineering, including object-oriented design, system design, and testing practices such as TDD 
• Demonstrated ability to build simple, maintainable, and scalable systems with a focus on long-term reliability 
• Proficiency in one or more modern technologies, Python, PHP, JavaScript, or TypeScript, with the ability to learn new technologies quickly 
• Deep experience working with Git in collaborative environments, including managing shared codebases, conducting code reviews, and maintaining a high bar for quality 
• Ability to operate effectively in an AI-native workflow using AI as a collaborator to explore solutions and accelerate development, while applying strong judgment to ensure correctness, quality, and maintainability 
• Clear thinking and strong problem-solving ability, with the capacity to break down complex problems into simple, well-structured solutions 
• A strong sense of ownership — you take responsibility for outcomes, care deeply about quality, and are not comfortable shipping work that does not meet your standards.

Benefits

Life at Incubyte 

We are a remote-first company with structured flexibility. Teams commit to shared rhythms during core hours, ensuring smooth collaboration while maintaining autonomy. Twice a year, we come together in person for a co-working sprint and once a year for a retreat - with all travel expenses covered. 

Our environment is built for crafters: pairing, refactoring, experimenting with AI, and pushing the boundaries of software excellence. We are all lifelong learners, and our work is our passion. 

Perks

• Dedicated learning & development budget. 
• Sponsorship for conference talks. 
• Comprehensive medical & term insurance. 
• Employee-friendly leave policies. 
• Home Office fund 
• Medical Insurance

Forward Deployed Implementation Engineer

About the Role

We are looking for a Forward Deployed Implementation Engineer who can work closely with customers and internal teams to successfully implement and operationalize our product.

This is a customer-facing, execution-oriented role that sits at the intersection of Product, Engineering, Accounting, and Customer Operations.

You will own implementations from start to finish — understanding customer requirements, configuring the product, setting up workflows and integrations, troubleshooting issues, coordinating with Engineering/Product, and ensuring customers are successfully onboarded and operational.

This role is ideal for someone who enjoys solving real customer problems, working with data and systems, understanding accounting workflows, and getting things across the finish line.

What You’ll Do

• Own the end-to-end implementation of the product for new and existing customers.
• Understand customer business processes, accounting workflows, and operational requirements.
• Configure customer/entity setups, workflows, recipes, rules, and product features based on business requirements.
• Work with accounting and finance teams to understand their requirements and translate them into product configurations.
• Set up and validate integrations with systems such as accounting platforms, POS systems, banking platforms, and other third-party systems.
• Investigate implementation issues, data discrepancies, integration failures, and workflow problems.
• Work closely with Engineering and Product teams to troubleshoot issues and drive them to resolution.
• Clearly communicate customer requirements, implementation blockers, and product gaps to internal teams.
• Coordinate across multiple stakeholders and ensure implementation tasks are completed on time.
• Test configurations and integrations end-to-end before customer rollout.
• Perform data validation and reconciliation to ensure the system is producing accurate results.
• Support customer/accounting teams during onboarding and transition them successfully to the product.
• Create implementation documentation, playbooks, and repeatable processes.
• Identify recurring customer problems and work with Product/Engineering to improve the product and implementation process.
• Provide feedback from customer implementations to help shape product improvements.

What We’re Looking For

• 2–6 years of experience in implementation, solutions engineering, technical consulting, customer success engineering, business systems, or a similar role.
• Strong problem-solving and analytical skills.
• Comfortable working directly with customers and internal technical teams.
• Ability to understand complex business processes and translate them into system configurations.
• Strong understanding of data, workflows, integrations, and APIs.
• Good understanding of accounting/finance concepts and workflows is highly preferred.
• Comfortable working with spreadsheets and data; SQL knowledge is a strong plus.
• Ability to investigate issues independently and identify the root cause rather than simply escalating problems.
• Strong ownership mindset — you take a problem from "this is broken" to "this is solved."
• Excellent communication skills, both written and verbal.
• Comfortable working in a fast-moving environment where requirements can evolve.
• Ability to manage multiple customer implementations and prioritize effectively.

Good to Have

• Experience with accounting systems such as NetSuite, QuickBooks Online, or similar platforms.
• Experience working with POS systems such as Toast or similar platforms.
• Experience with banking/payment integrations.
• Experience working with REST APIs and integration troubleshooting.
• SQL/Postgres experience.
• Experience working with accounting data, reconciliations, journal entries, revenue, expenses, payouts, or financial reporting.
• Experience in SaaS implementation or B2B software.
• Experience working with Engineering/Product teams in a startup environment.

What This Role Is NOT

This is not a traditional software engineering role where your primary responsibility is writing application code.

You will work closely with Engineering, but your primary responsibility is to:

Understand → Configure → Implement → Validate → Troubleshoot → Deploy → Drive Adoption

You should be comfortable getting hands-on with the product, customer data, integrations, and workflows to solve problems.

Who Will Succeed in This Role?

You will do well if you are someone who:

• Enjoys solving ambiguous problems.
• Can speak comfortably with both customers and engineers.
• Is technically curious and can understand how systems and integrations work.
• Doesn't wait for someone else to solve a problem.
• Can dig into data to find what is actually happening.
• Is comfortable working with accounting/finance teams.
• Enjoys taking ownership of implementations.
• Can balance customer requirements with product capabilities.
• Is willing to get hands-on and figure things out rather than simply following a checklist.

Why Join Us?

You will have significant ownership over how customers successfully adopt the product.

You will work across customers, accounting teams, Product, and Engineering, giving you exposure to both business and technology.

This role is a great opportunity for someone who wants to grow into a Solutions Engineer, Implementation Lead, Product Specialist, or Forward Deployed Engineer while working on real-world customer problems.

About the company

We are an AI-powered media intelligence platform that transforms video and audio archives into searchable, reusable and monetizable assets.

The platform combines media processing, search, multimodal AI and large-scale content understanding to help users discover meaningful moments within vast media libraries. We are an early-stage startup where engineers work across disciplines and contribute directly to

products used by customers.

Role Overview

We are looking for an Assistant Software Engineer who wants to grow rapidly across backend engineering, AI integration, media processing and platform operations.

You will work closely with the AI, platform and product engineers to build the supporting systems that make our AI capabilities usable, testable and reliable. You will help bridge the gap between models, services, data and product workflows while learning from experienced engineers across multiple disciplines.

What You Will Work On

AI Operations & Evaluation (Approx. 50%)

• Integrate AI services into backend workflows.
• Validate AI outputs against expected results.
• Build scripts and tools for evaluation and testing.
• Maintain datasets used to assess model quality.
• Support retrieval and search workflows.
• Investigate incorrect or unexpected AI outputs.

Media & Data Processing (Approx. 35%)

• Develop and maintain media-processing jobs.
• Support subtitle, transcript and metadata pipelines.
• Process and validate structured AI outputs.
• Assist with data preparation and annotation workflows.
• Build automation around ingestion and content processing.

Platform & Engineering Operations (Approx. 15%)

• Support deployment and monitoring workflows.
• Maintain development environments and internal tools.
• Assist with CI/CD automation.
• Help improve observability and operational dashboards.
• Support troubleshooting and production investigations.

What Success Looks Like

Within your first six months:

• AI evaluation workflows are easier to run and maintain.
• Model outputs can be tested and verified consistently.
• Media-processing jobs are reliable and observable.
• Engineering teams spend less time on repetitive operational tasks.
• Documentation and internal tooling improve team productivity.

Requirements

Must Have

• Good knowledge of Python.
• Experience building meaningful personal, academic or professional software projects.
• Understanding of REST APIs and JSON.
• Working knowledge of SQL and relational databases.
• Familiarity with Git and Linux.
• Ability to debug issues systematically.
• Strong curiosity and willingness to learn unfamiliar technologies.
• Good written and verbal communication skills.

Nice to Have

• FastAPI or Flask.
• PostgreSQL.
• Docker.
• Background workers or asynchronous processing.
• Computer vision, LLMs or AI APIs.
• Vector search, embeddings or RAG.
• FFmpeg or media-processing tools.
• AWS, Azure or GCP.
• JavaScript or TypeScript.
• CI/CD tooling.

We Value

• Curiosity over credentials.
• Ownership over job titles.
• Learning over knowing everything.
• Engineers who ask good questions.
• Engineers who can explain how things work.
• People who are comfortable working across multiple disciplines.

Why Join Us

• Work directly with experienced AI, platform and product engineers.
• Gain exposure to media AI, search and retrieval systems.
• Build real production systems from an early stage.
• Grow into backend, AI, platform or full-stack engineering paths.
• Help shape a product from its earliest stages.

About the role

We are building AI systems that read, understand and act on real business documents, bank statements, financial reports, policy documents and forms and putting them into production where accuracy and cost both matters.

This is not a research role and it is not a prompt-writing role. You will own features end to end: pick and deploy open-source models, build the pipelines around them, measure whether they actually work on our documents, drive the cost per document down, and keep the whole thing running in production.

You will work closely with the engineering and product teams, and your work will be directly used by business users from day one.

What you will do

Deploy and evaluate open-source models

• Select, deploy and benchmark open-source LLMs and vision-language models for specific, narrow use cases not general chat.
• Build evaluation sets from real documents and define what "good" means numerically (field-level accuracy, extraction recall, hallucination rate) before shipping.
• Run structured comparisons between models and approaches, and write up the trade-offs so the team can make a decision.
• Apply quantization, batching and other optimizations to fit models into a sensible GPU budget.

Build and optimize AI orchestration

• Design multi-step pipelines that combine deterministic code, ML models and LLM calls and know when not to use an LLM.
• Optimize for latency, cost and reliability: caching, batching, request routing, fallback tiers, retries and graceful degradation.
• Instrument pipelines so failures are visible and traceable rather than silent.

Ship to production

• Package models and services with Docker, expose them behind clean APIs, and deploy them to our GPU and CPU infrastructure.
• Handle the unglamorous production concerns: cold starts, timeouts, concurrency limits, versioning, rollback and monitoring.
• Own on-call-style responsibility for the AI features you build, including cost tracking.

Must-have skills

Programming & engineering

• Strong Python: type hints, async/await, dataclasses/Pydantic, clean module design, testing.
• REST API development with FastAPI (or Flask/Django with a willingness to move to FastAPI).
• Git, code review discipline, and the ability to write code someone else can maintain.
• Comfortable in Linux and on the command line.

Machine learning fundamentals

• Working knowledge of PyTorch and the Hugging Face ecosystem (transformers, tokenizers, accelerate).
• Understanding of inference-time concepts: tokenization, context windows, batching, precision (FP16/BF16/INT8), memory footprint.
• Ability to read a model card and a paper well enough to judge whether a model fits a use case.

Document processing

• Hands-on experience with at least two of: pypdfium2, PyMuPDF, pdfplumber, pdfminer.six, Docling, Unstructured, Surya, DocTR, LayoutLM family.
• Practical OCR experience (Tesseract, PaddleOCR, or a cloud OCR) and an understanding of when OCR is the wrong tool.
• Experience extracting tables from PDFs and dealing with merged cells, multi-line rows, and inconsistent column layouts.

Strongly preferred

You will be a much stronger candidate with any of these. We do not expect all of them.

Model serving & optimization

• vLLM, TGI, Ollama, llama.cpp, or Triton Inference Server.
• Quantization formats and tooling: GGUF, AWQ, GPTQ, bitsandbytes, ONNX Runtime, INT8 export.
• Serverless GPU platforms: Modal, RunPod, Replicate, Baseten including cold-start and container-lifecycle management.
• LoRA / QLoRA fine-tuning with PEFT for narrow, task-specific improvements.

Vision-language models

• Practical use of open VLMs: Qwen2.5-VL, InternVL, Granite Vision, Molmo, Phi-Vision, or similar.
• Awareness of where VLMs hallucinate especially on numeric and financial content and patterns for constraining them (using the model for layout only, sourcing values from the text layer, constrained decoding).

Orchestration & pipelines

• Workflow orchestration: Dagster, Airflow, Prefect, or Temporal.
• Async job patterns: Celery, RQ, or platform-native spawn/poll patterns.
• LLM orchestration frameworks (LangGraph, LlamaIndex, Haystack) with the judgement to know when plain Python is a better answer.
• Structured output enforcement: Instructor, Outlines, XGrammar, JSON schema / tool-use modes.

Evaluation & observability

• Building golden datasets and regression suites for extraction tasks.
• Eval tooling: promptfoo, DeepEval, Ragas, or in-house harnesses.
• LLM tracing and monitoring: Langfuse, Arize Phoenix, LangSmith, OpenTelemetry.

Nice extras

• Rule engines and policy evaluation (Open Policy Agent / Rego, Drools, rule-engine).
• Experience in fintech, lending, insurance or accounting documents.
• Handling of PII and data-security practices in document pipelines.
• Contributions to open-source ML or document-processing projects.

Why join us

• Real production ownership from month one your work goes to actual users, not a demo.
• Genuinely hard technical problems in document AI, not wrappers over an API.
• Small team, short decision cycles, direct access to leadership.
• Budget and freedom to evaluate and adopt new open-source models as they land.

To apply: send your CV along with a short note on one AI system you have taken to production what it did, what the accuracy was, and what broke.

Forward-deployed engineers (FDEs) are Mactores' services layer. You embed with the customer's team, own outcomes from discovery through the production cutover, and personally carry the delivery commitment.

The agent platform we deploy absorbs 60–70% of engagement work, discovery, assessment, design, and testing. You absorb the judgment: target architecture, refactoring trade-offs, model selection, cutover strategy, and the decisions an agent platform cannot make. The agent absorbs scale. You absorb judgment. 

This is not a staff-augmentation seat and not an advisory role. You ship.

What you will do?

• Deliver production agentic AI systems and AWS modernization engagements on committed dates across three pillars: Data Platform Modernization, Application & Database Modernization, and AI Agents for Apps.
• Build and productionize AI agents, orchestration, retrieval pipelines, evaluation harnesses, observability running against real customer data, not demo data.
• Convert existing products into agents: expose product functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
• Convert existing Business processes into agents: expose process functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
• Embed directly with customer engineering teams. Run architecture sessions, defend design decisions, and align stakeholders from VP Engineering to CTO.
• Make agent decisions traceable and defensible, validation runs in parallel with live workloads, and outputs hold up to internal audit and regulators (HIPAA, PCI-DSS, FSI-grade governance where the vertical demands it).
• Feed field experience back into the platform and practice: your deployment patterns, integration playbooks, and edge cases shape how we deliver.

What are we looking for?

• Excellent communication skills (English) — verbal and written. Non-negotiable. You will present architecture to customer CTOs, write documents that hold up in audit, and defend judgment calls in the room. If you can build but not explain, this role is not a fit.
• You have shipped production agentic AI systems on AWS. Not POCs, not notebooks — systems running in production for real users. This is the primary qualification. Be prepared to walk through what you shipped, the decisions you made, and what broke.
• Deep understanding of agentic architecture — you can design an agent system from first principles and explain why each component exists:
• Agent design patterns: single-agent vs. multi-agent systems, supervisor/orchestrator patterns, hierarchical agent topologies, planner–executor separation, and when each applies.
• Orchestration: building and operating orchestrator agents that decompose tasks, route work to specialist agents or tools, and manage state across multi-step workflows (LangGraph, Strands Agents, CrewAI, or equivalent).
• Memory: short-term/working memory (context management, conversation state) and long-term memory (episodic and semantic stores, vector- and graph-backed retrieval), and the production trade-offs of each.
• Reflection and self-correction: critique loops, self-evaluation, retry-with-feedback patterns, and evaluation harnesses that catch agent failures before customers do.
• Tool use and function calling: schema design, tool-selection reliability, error handling, and agent-to-agent composition.
• RAG and retrieval pipelines: chunking, embedding, hybrid retrieval, reranking, and grounding agent decisions in customer data.
• Strong AWS production experience: Amazon Bedrock and AWS AI services, plus core platform services (Lambda, API Gateway, DynamoDB, RDS/Aurora, Glue, EMR, Redshift, Kinesis, or similar depending on specialization).
• Solid software engineering fundamentals Python, TypeScript, CI/CD, infrastructure-as-code, testing-driven development discipline.
• Experience with data or application modernization (database migration, legacy refactoring, data platform builds) is a strong plus, since agents run against these workloads.
• Indicative experience: roughly 3–10 years in engineering roles, with agentic AI / GenAI as your current day job. We have demonstrated agent-native expertise over tenure — an engineer with 3–4 years of hands-on agentic AI work typically outperforms a 12-year generalist on this work.

You'll be preferred if you've:

• US English verbal and written fluency 
• Delivery experience in one or more of our verticals: Financial Services, Healthcare & Life Sciences, Internet & Software, Manufacturing, or Telco/Media/Entertainment/Gaming/Sports.
• Model tuning and fine-tuning: systematic prompt engineering and optimization; parameter-efficient fine-tuning (LoRA/QLoRA or similar); instruction tuning; working knowledge of RLHF/DPO; sound judgment on when to fine-tune vs. prompt vs. RAG; and evaluation of tuned models against baselines. Fine-tuning experience on Amazon Bedrock or SageMaker is a plus.
• Experience with compliance-sensitive AI systems (HIPAA, PCI-DSS, SOC 2, data residency).
• Knowledge graph, code-analysis (AST), or CDC/streaming experience (Debezium, Kafka/MSK).
• Solid software engineering fundamentals — Java, C++, Go Lang, .Net, Rust
• Prior customer-facing consulting or forward-deployed experience.
• AWS certifications (Solutions Architect Professional, Machine Learning Specialty, or Data Analytics).

Why This Role?

• You own outcomes, not tickets. FDEs carry the delivery commitment personally — architecture, judgment, and cutover are yours.
• You work agent-native from day one. Our delivery model would not function without agents. You build with the platform, not around it.
• You ship. Engagements measured in weeks to production, legacy retired, outcomes named. No archived pilots.
• You compound. Field delivery informs the Aedeon platform roadmap; the platform's growth expands what you can deliver. Few engineering roles sit in that loop.