Forward Deployed Product Manager

Roles & Responsibilities

What you'll do

• Embed with enterprise customers to understand their business processes, systems landscape, and data. Translate that into concrete use cases for AI agents, automations, and applications.
• Build working solutions on the UnifyApps platform yourself, using the no-code builders, integrations, and data layer. Ship, iterate, and get to production.
• Own the technical and functional design of deployments across systems like Salesforce, Workday, SAP, ServiceNow, core banking and telecom stacks.
• Work with customer stakeholders from process owners up to CXOs. Run discovery workshops, demos, solution reviews, and go-live.
• Define success metrics for each deployment and hold the deployment to them.
• Feed patterns and gaps back to the core product and engineering teams. Turn repeated custom work into platform capability.
• Support pre-sales and pilot conversations where deep product depth is needed.

Ideal Candidate

1Strong Forward Deployed Product Manager Profile with robust exposure to client facing delivery

2Mandatory (Experience 1) - Must have 4+ years of product management experience with a track record of shipping successful products with product companies

3Mandatory (Experience 2) Must have hands-on experience driving platform deployment and integrations for enterprise clients — acting as the technical/product owner to understand client requirements, design the solution, and connect the platform with enterprise systems, APIs, and data sources.

4Mandatory (Tech skill 1) - Must have strong technical aptitude with hands-on experience in GenAI and API integrations

5Mandatory ( Skill 1): Must have experience working closely with designers, engineers, testers and other stakeholders

6Mandatory (Skill 2): Must be very good at making impactful presentations, with strong communication and presentation skills

7Mandatory (Skill 3) - Must have a bias to build, with comfort in ambiguity and evolving scope

8Mandatory (Skill 3): Must be able to lead and mentor a team of Forward Deployed Engineers, helping them grow into future product leaders

9Mandatory (Company) - Tier1 B2C product companies or Series B+ funded B2B product companies

10Preferred (AI Depth) - Exposure to enterprise AI deployments in production, including evaluation, guardrails, and observability of agents.

Strong Forward Deployed Product Manager Profile with robust exposure to client facing delivery

2

Mandatory (Experience 1) - Must have 4+ years of product management experience with a track record of shipping successful products

3

Mandatory (Experience 2) Must have hands-on experience driving platform deployment and integrations for enterprise clients — acting as the technical/product owner to understand client requirements, design the solution, and connect the platform with enterprise systems, APIs, and data sources.

4

Mandatory (Tech skill 1) - Must have strong technical aptitude with hands-on experience in GenAI and API integrations

5

Mandatory ( Skill 1): Must have experience working closely with designers, engineers, testers and other stakeholders

6

Mandatory (Skill 2): Must be very good at making impactful presentations, with strong communication and presentation skills

7

Mandatory (Skill 3) - Must have a bias to build, with comfort in ambiguity and evolving scope

8

Mandatory (Skill 3): Must be able to lead and mentor a team of Forward Deployed Engineers, helping them grow into future product leaders

9

Mandatory (Company) - Tier1 B2C companies or Series B+ funded B2B companies

10

Preferred (AI Depth) - Exposure to enterprise AI deployments in production, including evaluation, guardrails, and observability of agents.

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.

Blitzy is a Cambridge, MA based AI software development platform on a mission to revolutionize the software development life cycle by autonomously building custom software to unlock the next industrial revolution. We're transforming how enterprises build software, turning enterprise requirements into production-ready code with an agentic software development platform that can autonomously execute 80% of the quantum of software development work. We're backed by multiple tier 1 investors, and have proven success as founders of previous start-ups.

Our Culture

Who we are:

Led by two pioneering co-founders we are one of the fastest growing companies in the U.S., creating our own category of enterprise autonomous software development. We automate thousands of hours of software development for our customers, which includes strong representation within the Fortune 500.

How we work:

• We move Blitzy Fast: Time is both our company’s and our clients’ most precious asset. We move quickly and decisively to innovate internally and deliver exceptional software externally.
• Championship Mindset: We operate like a professional sports team. We win as a team by holding ourselves and each other to high standards, collaborating in-person, and remaining focused on the mission.
• Passion for Invention: We’re pushing the frontier of what’s possible, requiring constant innovation and iteration.
• We Work for the Customer: We focus on delivering outsized value to the customers we work with and expanding those relationships into deep, meaningful partnerships.
• We believe in being ‘everyday athletes’: taking care of ourselves so we can bring our best minds to work. We promote great sleep, movement, and restorative activities for optimal mental performance. It makes for a happier and more productive team.

About the Role

We're looking for a Forward Deployed Engineer to join our Pune HQ2 team and work at the intersection of cutting-edge AI and complex enterprise software. You'll embed directly with customer engineering teams, writing production code, architecting solutions, and helping customers transform how they build software with Blitzy.

This isn't a traditional solutions engineering role. We're looking for a hands-on engineer who thrives in ambiguous environments and is equally comfortable debugging integrations, building Java/Spring Boot services, participating in sprint planning, or whiteboarding architecture with a CTO.

The role centers on our Java/Spring Boot enterprise stack, spanning REST services, event-driven microservices, integrations, and production operations. You'll work closely with US-based engineering and customer teams, with regular time zone overlap for collaboration and handoffs.

You'll also influence our product roadmap through direct customer feedback, identifying new use cases and technical challenges that shape Blitzy's evolution. High performers may have opportunities to visit our Cambridge, MA headquarters and transition into core product or platform engineering as we scale.

Responsibilities

• Deploy Blitzy into complex enterprise environments, navigating technical constraints and translating customer business goals into practical technical solutions
• Embed with customer teams during critical implementations, partnering with engineering leaders on sprint planning, architecture reviews, technical decisions, and production deployments
• Build production-quality integrations, services, and extensions using Java 17 and Spring Boot 3.x, including Spring MVC, Spring Data JPA, Spring Security, and Actuator
• Build and evolve REST APIs using a contract-first approach with OpenAPI/Swagger, with data persistence through PostgreSQL, Hibernate/JPA, and Liquibase
• Package and deploy containerized services with Docker and manage dependencies through Maven, including BOMs and multi-module projects
• Develop custom solutions, reusable patterns, scripts, and tools that accelerate customer adoption and demonstrate Blitzy’s capabilities in enterprise environments
• Debug complex issues spanning customer infrastructure, integrations, microservices, and the Blitzy platform, supporting production operations, health checks, metrics, and configuration management
• Drive successful POCs and technical demonstrations that showcase advanced use cases, deliver measurable value, and expand into enterprise-wide deployments
• Maintain high standards for testing, code quality, performance, and observability using JUnit 5, Cucumber/Gherkin, JMeter, Git, Logback/SLF4J, SpotBugs, and SonarQube
• Identify recurring customer needs, technical patterns, and expansion opportunities that inform product priorities and enterprise adoption
• Collaborate with product and engineering teams to translate field experience into platform improvements, technical documentation, reusable best practices, and contributions to the core product

Qualifications

• 8+ years of software engineering experience with a track record of shipping production code in enterprise environments
• Recent or past experience in global technology consulting environments, with direct engagement with the customer's engineering leadership teams.
• Deep hands-on expertise with Java 17, Spring Boot 3.x, Spring MVC, Spring Data JPA, Spring Security, Hibernate/JPA, and Actuator
• Strong experience with REST APIs, OpenAPI/Swagger, PostgreSQL, Liquibase, Maven, Docker, and modern testing practices including JUnit 5, Cucumber/Gherkin, and JMeter
• Experience building and operating microservices with CI/CD, cloud architecture, observability, production monitoring, and tools such as Micrometer, Dynatrace, SpotBugs, and SonarQube
• Knowledge of enterprise modernization and distributed systems, including Java 8 → Java 17 upgrades, monolith → microservices migrations, Kafka, Avro, Schema Registry, caching, and resilience patterns
• Strong understanding of Agile/Scrum and modern software development practices, with the ability to communicate complex technical concepts to audiences ranging from interns to CTOs
• Experience in customer-facing engineering, developer tools, AI/ML, or enterprise modernization is a plus, along with technical thought leadership through blogs, talks, or open-source contributions
• Passion for AI-powered software development and willingness to work in person from Pune, India, with overlap for US-based teams

U.S. GenAI startup, Pune Office, Private Limited Company

Full Time Employment directly to our Private Limited Company. We are committed to an enduring and robust presence in Pune. Our goal is to enable you to have a long enduring career at Blitzy with opportunity for advancement throughout your career at the company. If you want a role you can turn into a career, read on! If you are looking for a short term arrangement, we recommend you look elsewhere!

Blitzy is an equal opportunity employer committed to building a diverse and inclusive team. We believe different perspectives make us stronger.

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.

Senior Product Manager – AI / B2B SaaS

Location: Bengaluru

Work Mode: Work from Office

Experience: 5+ years

Department: Product Management

About LeadSquared

LeadSquared is a leading Sales CRM for high-velocity revenue teams, helping businesses manage leads, sales processes, customer interactions, marketing, and revenue workflows at scale.

We serve businesses where large sales teams manage high volumes of leads and customer interactions, making speed, prioritisation, automation, and effective sales execution critical to revenue growth.

Role Overview

We are looking for a Senior Product Manager – AI to own the roadmap and end-to-end execution of AI products within LeadSquared.

Key Responsibilities

• Own the roadmap, prioritisation, and end-to-end execution for your AI product area.
• Understand customer problems and identify where AI can create meaningful, scalable value and translate them into clear product requirements and scalable solutions.
• Drive products from discovery and 0→1 development through launch, adoption, and iteration.
• Build and scale end-user-facing AI experiences that work reliably within enterprise workflows. 
• Make informed product decisions considering quality, latency, and scale trade-offs in production AI systems.
• Work closely with Engineering, Design, AI/ML, Data, and GTM teams to ship high-quality products.
• Define and track product adoption, engagement, customer outcomes, and business impact.
• Drive GTM, adoption, and continuous product optimisation.

What We're Looking For

• 5+ years of Product Management experience.
• 2+ years of hands-on Product Management experience building AI products.
• Strong B2B SaaS product experience.
• Experience in 0→1 product development and launches.
• Strong product discovery, prioritisation, roadmap management, and execution skills.
• Familiarity with AI evaluations (evals), observability, guardrails, and human oversight.
• Data-driven approach with strong understanding of product metrics.
• Excellent communication, problem-solving, and stakeholder management skills.
• Candidates from Tier 1 / Tier 1.5 / Tier 2 colleges preferred.

Good to Have

• Experience building products using GenAI, LLMs, RAG, AI Agents / Agentic AI, or ML-based decision systems.
• Experience in CRM, SalesTech, MarTech, or Enterprise SaaS.
• Experience building AI products for sales, revenue, operations, or other enterprise users.
• Experience taking an AI capability from an early prototype to a reliable, scalable enterprise product.
• Candidates who have built AI side projects will have a preference. Share links to live products, prototypes, demos, GitHub repositories, agents, or apps you’ve built.
•  

Strong Senior Project Manager Profile with enterprise B2B SaaS, client-facing delivery experience

2

Mandatory (Experience 1): Must have 5+ years of project management experience with significant client-facing delivery time (non-negotiable), including experience in a B2B SaaS company serving enterprise customers

3

Mandatory (Experience 2): Must have managed complex enterprise projects end-to-end, from requirement/planning through successful delivery and go-live

4

Mandatory (Experience 3): Must have managed multi-country / multi-region rollouts (preferably North America and/or Europe), coordinating India-based delivery teams with customers/stakeholders in Western markets

5

Mandatory (Experience 4): Must have delivered technically complex / analytics-driven projects (AI/ML, Image Recognition, or Data Analytics)

6

Mandatory (Tech skill): Must have strong working knowledge of SQL, APIs, and Master Data Management (MDM)

7

Mandatory (Experience 5): Must have experience with SOWs, change requests, or RFPs, managing scope, timelines, risks, and dependencies using Waterfall or Agile

8

Mandatory (Ownership): Must be the accountable owner of the engagement — primary client point of contact, owning the project plan and go-live end-to-end — not a hands-on specialist executing the build

9

Mandatory (Skill): Must have strong facilitation, communication, stakeholder-management, and executive-level reporting skills across cross-functional, geographically distributed teams

10

Mandatory (Note): CTC is inclusive of variable

11

Preferred (Experience): Prior experience with customer onboarding

12

Preferred (Certification): PMP, CAPM, ITIL, or CSM.

We are looking for a highly strategic and execution-oriented Senior Product Manager / Product Manager – CMS & Digital Experience Platform to drive the evolution of Milestone’s AI-first CMS and Digital Experience capabilities.

This role requires a product leader who can deeply understand enterprise customer problems, market trends, AI-led content evolution, SEO/GEO dynamics, Personalization, and modern web experience requirements while partnering closely with Engineering, Design, GTM, Customer Success, and Enterprise Customers.

You will own product strategy, roadmap prioritization, customer outcomes, adoption metrics, Customer communication, and feature execution for our next-generation CMS platform.

Problems We Are Looking to Solve

• Enterprise-scale AI-driven content management
• Discoverability across Search and Generative AI ecosystems
• AI-powered content optimization and governance
• SEO/GEO-native digital experiences
• Conversion-focused, hyper-personalized customer journeys

Key Responsibilities

Strategic Vision & Leadership

• Define and execute product vision and roadmap\
• Drive AI-first product innovation
• Conduct market and competitive analysis
• Translate customer pain points into scalable solutions
• Understand customer churn and derive strategies to reduce future churn

Operational Excellence & Growth

• Own product lifecycle from ideation to launch
• Create PRDs, user stories, and documentation
• Drive roadmap execution with Engineering teams
• Monitor adoption, engagement, and retention KPIs

Innovation & Culture

• Champion AI-native product thinking
• Foster innovation and accountability culture
• Evangelize product capabilities internally and externally
• Stay ahead of CMS, Digital asset management and AI trends

Cross-Functional Alignment

• Collaborate with Engineering, UX, Marketing, Sales, and CS teams
• Partner with Product Marketing for GTM initiatives
• Support enterprise sales cycles and customer workshops

Customer Impact

• Act as voice of the customer
• Improve customer adoption and success outcomes
• Ensure measurable business impact

Talent & Empowerment

• Mentor junior product team members
• Promote ownership and continuous learning
• Strengthen product management best practices

Ideal Candidate Profile

The ideal candidate is a customer-centric product leader with strong experience in B2B SaaS platforms, CMS technologies, AI-led digital experiences, and enterprise-scale product management.

Required Experience & Qualifications

• 8+ years of Product Management experience in B2B SaaS
• Experience with CMS, Digital Experience Platforms, or Martech
• Strong understanding of APIs and modern web architectures
• Experience collaborating with Engineering and Design teams
• Strong analytical and communication skills

Key Focus Areas

• AI-native CMS innovation
• SEO/GEO-first digital experiences
• Enterprise content scalability
• Product-led growth and adoption

Capabilities

1. Product Strategy & Roadmapping

2. Enterprise SaaS Product Management

3. AI-first Product Thinking

4. Agile Product Development

5. Customer Discovery & Validation

6. Data-Driven Decision Making

7. Stakeholder Management

8. GTM Collaboration

KPI and Metrics

1. Product Adoption

2. Customer Engagement & Retention

3. Time-to-Market

4. Customer Satisfaction

5. Platform Performance

6. Revenue Influence

7. Product Usage Growth

8. Customer Success Outcomes

Qualifications

• Bachelor’s degree in Engineering, Computer Science, or related field
• MBA preferred
• Product or Agile certifications are a plus

What We Offer

• Competitive compensation and incentives
• Opportunity to build AI-first products
• Exposure to global enterprise customers
• Career growth and leadership opportunities
• Collaborative and innovative culture

How to Apply

Interested candidates are invited to submit their resume and a cover letter outlining their qualifications and vision for the role plus previous experience align with key attributes, responsibilities, clear wins or learnings.   

Why Milestone

• AI-first Digital Experience Platform
• SEO/GEO-native CMS leadership
• Founder-led innovation culture

About the Role

We are looking for a Forward Deployed Engineer with strong hands-on experience in Databricks and AI-assisted software development using Cursor. The role is suited for an engineer who can work directly with clients and internal teams to understand business problems, rapidly build solutions, and take them from prototype to production.

The ideal candidate should have strong expertise in Python, SQL, Databricks, PySpark, data engineering, APIs, and modern AI-assisted development workflows, along with excellent problem-solving and client-facing skills.

Key Responsibilities

Forward Deployed Engineering

• Work directly with clients and stakeholders to understand business and technical requirements.
• Translate business problems into scalable technical and data solutions.
• Rapidly prototype, test, iterate, and productionize solutions.
• Collaborate with engineering, data, AI, and delivery teams to implement customer solutions.
• Troubleshoot production issues and continuously improve deployed solutions.
• Act as a technical bridge between clients and internal engineering teams.

Databricks & Data Engineering

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

Cursor & AI-Assisted Development

• Use Cursor and AI-assisted development workflows to accelerate software development, debugging, refactoring, and documentation.
• Effectively use AI coding assistants to understand existing codebases and develop new features.
• Apply appropriate engineering judgment to review, validate, test, and secure AI-generated code.
• Use AI-assisted development for rapid prototyping and proof-of-concept development.
• Work with modern AI/LLM APIs and tools where required for customer solutions.
• Stay current with emerging AI-assisted software engineering practices.

Production & Deployment

• Develop production-ready applications, APIs, and data pipelines.
• Work with Git, CI/CD, APIs, containers, and cloud environments.
• Monitor application and pipeline performance and resolve production issues.
• Ensure solutions meet requirements for scalability, security, reliability, and maintainability.
• Collaborate with Data Scientists and ML Engineers to integrate AI/ML capabilities into production systems.

Required Skills & Experience

• 4+ years of experience in Software Engineering, Data Engineering, AI 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 building production-grade data pipelines.
• Hands-on experience with Cursor or similar AI-powered coding assistants.
• Strong understanding of REST APIs and system integrations.
• Experience working with cloud platforms such as AWS, Azure, or GCP.
• Strong debugging, problem-solving, and analytical skills.
• Excellent communication and client-facing abilities.

Preferred Skills

• Experience with Databricks Unity Catalog, Workflows, and MLflow.
• Experience with Generative AI / LLM applications.
• Knowledge of Claude, OpenAI, Azure OpenAI, or other LLM platforms.
• Experience with RAG, vector databases, embeddings, or AI agents.
• Experience with Docker, Kubernetes, and CI/CD.
• Experience in a consulting, customer-facing engineering, or professional services environment.
• Exposure to Agile/Scrum methodologies.

Key Competencies

• Strong problem-solving and ownership mindset
• Ability to work in ambiguous and fast-paced environments.
• Strong client/stakeholder management skills.
• Ability to understand business requirements and convert them into technical solutions.
• Strong communication and presentation skills.
• Ability to rapidly learn new technologies and tools.
• Comfortable working with AI-assisted development while maintaining high engineering standards.

Education

• Bachelor's or master’s degree in computer science, Information Technology, Engineering, Data Science, or a related discipline.

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.