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.

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.

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.

Location: Hyderabad, India (home base), deployed at client sites in India. Occasional Middle East exposure possible.

About the Role

You will work as a senior AI consultant who embeds inside a customer's business. Your job is to learn how the business makes money, find the highest value problem, and build a working system that solves it.

You will not hand over a document and walk away. You will show working software early, own the roadmap, own the client relationship, and stay after go-live to run and improve the system.

Four behaviors define this role:

1. Go where the work happens. You work onsite with the customer, in the room where decisions are made.
2. Show working software early. You build a prototype in days, not a document in weeks.
3. One person owns the outcome. You are the single point of accountability for the result.
4. Stay after go-live. You keep running and improving the system after launch.

You are the single point of accountability. You are not a solo builder. A full KnackLabs engineering team in Hyderabad builds and runs the production systems behind you.

This role involves extended onsite deployments at client locations in other cities, sometimes up to six months at a stretch. Please apply only if you are ready for this way of working.

What you'll own

1. Discovery - Learn how the customer makes money. Find the highest value problem to solve first.
2. The prototype - Build a working prototype fast, using real or sample data, to prove the idea.
3. The roadmap - Decide what to build, in what order, and set clear success measures tied to business outcomes.
4. The build - Design and ship the production system with the Hyderabad engineering team. This includes data integration, agents, retrieval, and evaluations.
5. The client relationship - Be the trusted technical contact for the customer, from engineers to senior leaders.
6. Go live and after - Deploy the system, watch how it performs, fix problems, and improve it over time.
7. Feedback to the product - Share what you learn in the field so our platform and internal tools get better.

What we are looking for

1. Around 7 or more years of software engineering experience, including customer-facing or client delivery work.
2. Experience working at a consulting or professional services firm in a client-facing delivery role.
3. A full-stack development experience with strength in backend technologies.
4. Strong programming skills in Python. Working knowledge of TypeScript or JavaScript.
5. Production experience with large language models, including prompt engineering and agent development.
6. You build with AI coding tools like Claude Code as your default way of working. You have built real apps and agents this way, not just used it for document generation or review.
7. Experience building retrieval-augmented generation (RAG) systems: chunking, embeddings, vector databases, retrieval, and reranking.
8. Experience building and deploying AI systems.
9. Experience integrating with APIs and enterprise systems.
10. Experience with at least one cloud platform (AWS, Azure, or GCP).
11. Experience building evaluations to measure accuracy, safety, latency, and cost.
12. Clear communication. You can explain a technical choice to an engineer and to a business leader.
13. High ownership and comfort with ambiguity. You can take an unclear problem and turn it into a plan.
14. Willingness to work onsite at client locations in India for extended periods, and to travel as the work needs.

Nice to have

1. Experience deploying AI systems in regulated industries such as insurance, banking, or the public sector.
2. Experience with on-premises or private cloud (VPC) deployments.
3. Experience with observability and tracing tools such as LangSmith or Braintrust.
4. Experience with data engineering and pipelines.
5. A history of side projects, open source contributions, or products you shipped end-to-end.
6. Experience in embedded or forward-deployed roles before.

Stack and tools

1. Languages: Python and TypeScript.
2. Models: Claude and other frontier or open source models, chosen to fit the customer.
3. AI patterns: RAG, agents, prompt engineering, and evaluations.
4. Vector and retrieval: vector databases and retrieval pipelines.
5. Cloud: AWS, Azure, or GCP, on public or private cloud.
6. Integration: REST APIs and enterprise system connectors.

End to end implementation of a B2B system. The role requires working with the clients who have been onboarded and see through the implementation of the system. Familiarity with python based frameworks like Frappe is desirable but not mandatory.

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.

Full-Stack Engineer (Backend Heavy)

Experience: 4–6 Years | Function: Engineering — Product | Location: On-site

About Us

We’re building the next generation of AI-powered business software, and we’re looking for people who want to shape that future with us. With Lumen, we’re reimagining how users interact with CRM — moving beyond screens, menus and dashboards to an intelligent interface where users can simply ask AI to take actions, retrieve knowledge, generate insights and get work done. With Agent Studio, we’re enabling businesses to build, test and deploy their own AI agents for real-world workflows. And with Invorto, we’re bringing AI to voice, allowing businesses to create intelligent voice agents tailored to their customer and operational use cases.

What makes this especially exciting is the stage and scale of the opportunity. You’ll get to work on genuinely hard problems across LLMs, agents, reasoning, orchestration, voice AI, evaluation, reliability and enterprise security — not as isolated experiments, but as products used in real business workflows. You’ll have the opportunity to build zero-to-one, own meaningful parts of the product end-to-end, work closely with customers, experiment rapidly, and see your work reach production at scale.

Why join now? Because the playbook for enterprise AI is still being written. You won’t just be implementing someone else’s roadmap — you’ll help define the product, architecture and experiences that become that playbook. Expect high ownership, fast iteration, hard technical and product problems, direct customer impact, and the chance to build AI systems that have to work reliably in the real world — not just in a demo.

About the Role

We are looking for a Full-Stack Engineer with a strong backend bias to help build end-to-end product experiences across Lumen and Agent Studio. You will own features from database and API design through to the front-end experience, working closely with product and design to ship AI-powered experiences that real business users depend on every day.

What You’ll Do

Design and build backend services and APIs in Python that power core product and AI-agent features.

Build front-end interfaces and experiences that let users interact naturally with AI agents, insights and CRM workflows.

Own features end-to-end — from data modeling and backend logic to UI implementation, testing and release.

Work with product managers and designers to translate requirements into well-architected, scalable systems.

Integrate with LLM-based and agentic backend systems built by the AI/ML engineering team.

Optimize application performance, reliability and code quality across the stack.

Engage directly with customers and customer success teams to understand workflows, triage issues and inform roadmap decisions.

What We’re Looking For

4–6 years of professional full-stack engineering experience, with a clear backend-heavy skill set in Python.

Strong experience designing and building REST/GraphQL APIs, data models and scalable backend services.

Working proficiency with modern front-end frameworks (e.g., React) to build and integrate user-facing features.

Experience with relational/NoSQL databases, caching and cloud infrastructure.

Ability to move fast in a zero-to-one environment while maintaining code quality and system reliability.

Strong communication skills — this is a customer-facing role, and you will be expected to clearly articulate technical concepts, decisions and trade-offs to both technical and non-technical stakeholders, including customers.

Good to Have

Experience building features on top of LLM or AI-agent backends.

Prior experience in CRM, SaaS or enterprise business applications.

Exposure to real-time or voice-based product interfaces.

Build AI where the work actually happens.

Celeco works inside real businesses to understand critical workflows, ship production AI systems and stay through adoption.

We are hiring our first Intelligence Architect (Forward-Deployed AI Engineer).

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Full-time · Remote-first · Optional hybrid in Bengaluru

At least one year of professional engineering experience. Customer travel when the work requires it.

The role

An Intelligence Architect enters a customer environment with an unfinished question and leaves behind a working, measurable system.

This is a hands-on engineering role at the boundary of product, operations and customer delivery. You will learn the domain, inspect the existing systems and data, decide what should be built, and write the code that puts it into production.

What you will own

• Interview and shadow the people doing the work, then map the real workflow, including hand-offs, exceptions and workarounds.
• Understand the customer's application stack, APIs, data, identity model, security constraints and deployment environment.
• Turn business goals into a technical scope, system design, delivery plan and measures of success.
• Build across the stack: AI workflows, data pipelines, integrations, backend services and the interfaces people use.
• Choose the simplest reliable approach. The answer may combine agents, retrieval, rules and conventional software.
• Create evals from representative cases, define quality and failure metrics, inspect traces, red-team the system and set launch thresholds.
• Make practical trade-offs across accuracy, latency, cost, privacy, security and speed.
• Take prototypes into production with tests, monitoring, access controls, documentation and a plan for failure and recovery.
• Work beside customer teams during rollout and improve the system until it becomes part of the workflow.
• Turn what works into reusable components, evaluation sets and playbooks for future Celeco deployments.

You will probably thrive here if

• You have at least one year of professional software or product engineering experience.
• You have shipped a real system used by other people and can explain what you owned, what broke and what you changed.
• You are strong in Python or TypeScript and comfortable moving across unfamiliar codebases, APIs, databases and cloud services.
• You have built with language or multimodal models and understand prompting, structured outputs, retrieval, tool use and model failure modes.
• You use Cursor, Codex, Claude Code or similar tools as part of your engineering workflow, while still reviewing, testing and understanding the code you ship.
• You can create an evaluation set, choose useful quality metrics and improve a system through error analysis instead of prompt guesswork.
• You can speak with an operator, an engineering team and a senior leader without losing the thread of the problem.
• You work well with incomplete requirements, write clearly and surface risks early.
• You care about whether people use what you build and whether it changes a business outcome.
• You can commit full-time and travel to customer sites when discovery or rollout is better done in person.

Experience with cloud deployment, containers, CI/CD, observability, enterprise integrations, authentication or security is useful. We do not expect one person to arrive knowing every framework, cloud or industry.

What you will get

• Direct ownership of live customer problems from discovery through production.
• Close collaboration with Celeco's founders and customer leadership teams.
• Exposure to different industries, operating models and technical environments.
• The freedom to choose the technical approach and the responsibility to prove that it works.
• A role in defining Celeco's engineering methods, reusable systems and technical culture while the company is early.
• A remote-first setup, with the option to work together in Bengaluru.

If the work sounds like you but your background is unconventional, apply. We care more about what you have built, how you think and how quickly you learn than pedigree.