Senior Product Manager (Forward Deployed, Product company)

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

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

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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.

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

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

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

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

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

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

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

Job Title : Senior Product Manager (Forward Deployed)

Experience : 4 to 8 Years

Location : Bengaluru | Mumbai | Pune

Positions : 20

Work Mode : In-Office – 5 Days

Job Role :

We are looking for a Senior Product Manager – Forward Deployed to work closely with enterprise customers, understand complex business requirements, and translate them into AI-powered solutions using the UnifyApps platform.

The role requires a strong combination of Product Management, technical solutioning, enterprise integrations, GenAI, and customer-facing skills. You will own the customer journey from discovery and solution design through deployment, go-live, and outcome measurement.

Key Responsibilities :

• Work closely with enterprise customers to identify use cases and requirements.
• Build and deploy AI agents, automations and applications.
• Own technical & functional solution design and enterprise integrations.
• Conduct discovery workshops, demos and go-live activities.
• Define deployment success metrics and drive outcomes.
• Collaborate with Product & Engineering to turn customer needs into platform capabilities.
• Lead and mentor Forward Deployed Engineers.

Must Have :

✅ 4+ years of Product Management experience in product companies.

✅ Enterprise platform deployment & integration experience.

✅ Strong technical aptitude with GenAI & API integrations.

✅ Experience working with Engineering, Design & QA teams.

✅ Excellent presentation and communication skills.

✅ Strong problem-solving and execution mindset.

✅ Tier-1 B2C product or Series B+ B2B product company experience.

⭐ Preferred : Enterprise AI deployment experience, including agent evaluation, guardrails and observability.

Product Manager – AI/GenAI

Our AI platform that lets people ask questions of their enterprise data in natural language and get trustworthy answers. The hard part isn't the model — it's the fit between what the product can genuinely do and what customers actually need, over data that is never as clean as anyone hopes. We're hiring a Product Manager to own that fit: to do the detailed thinking on customer pain, competitive landscape, and where the technology is going, and to turn it into a sharp, defensible product proposition and roadmap. You'll work closely with the CTO (who currently carries this thinking) and become the person who owns the depth beneath it.

What you'll own

• Proposition & positioning. Take fuzzy capability and turn it into a clear, honest, defensible articulation of what Jiva is, who it's for, and why it wins. Kill the gap between what's sold and what's deliverable.

• Customer intelligence (within the current structure). Sales and Delivery own the customer relationship today; you build the customer picture through them —mining PoC post-mortems, win/loss, readiness audits, sales-call notes, and Delivery's ground truth. Extract real jobs-to-be-done, separate stated wants from underlying pain, and bring a synthesized customer voice into the room. Over time, earn a path to direct customer contact — but you'll start by getting more 

out of the signal the field teams already carry than anyone does today.

• Competitive & technology intelligence. Track the space — RAG, text-to-SQL, tabular/relational foundation models, agentic systems, the commercial players — and synthesize it into product direction. Know what's real and what's marketing.

• Requirements & roadmap. Translate the above into crisp requirements and a prioritized roadmap, in partnership with Engineering. Make the trade-offs explicit.

• Sales & Delivery enablement. Own the "what we can honestly promise" story so field teams stop overselling and start qualifying. You are the source of truth on capability boundaries.

Must-haves

• 5+ years in B2B / enterprise product management, ideally on data, analytics, AI/ML, or platform products.

• Demonstrated ability to frame a proposition — you can show us a product or feature where you turned raw capability into a positioning that won customers. Portfolio evidence, not a claim.

• Technical fluency in AI/data. You understand RAG, LLMs, text-to-SQL, and data quality well enough to judge feasibility vs. hype and to earn engineers' respect. You don't need to code; you need to reason.

• Customer discovery skill — and the resourcefulness to apply it 

indirectly. You've run structured discovery and can distinguish what customers say from what they need. Just as important here: you can extract sharp customer insight secondhand — through sales and delivery teams, deal histories, 

and product usage — because you won't have direct customer access on day one.

• Evidence-driven and structured. You validate before you evangelize. Strong written thinker — you can put a proposition on one page and defend it.

Nice-to-have

• Exposure to NL→SQL, semantic layers, knowledge graphs, or enterprise data governance.

• 0-to-1 or turnaround experience — comfort building the function, not just running it.

• Experience selling into or building for regulated / data-sensitive enterprises

Role: Forward Deployment Engineer (FDE)

Company: Comprinno (NASSCOM-incubated, AWS Advanced Consulting Partner)

Experience: 5 to 8 years

About the Role:

Comprinno is hiring a Forward Deployment Engineer to work directly with customers, identify business challenges, and turn them into working AI solutions on AWS. This is a customer-facing role that blends consulting, solution architecture, and hands-on AI engineering.

Key Responsibilities:

• Run discovery workshops with customer stakeholders and translate business problems into technical solutions.
• Design AI and cloud solutions using patterns such as RAG, AI agents, and workflow automation.
• Build POCs, prototypes, and MVPs on AWS (Bedrock, Lambda, S3, API Gateway, DynamoDB, OpenSearch, ECS/EKS) and support the move to production.
• Develop GenAI and agentic solutions, including prompt strategies, evaluation frameworks, and retrieval pipelines.
• Demo solutions, train customer teams, and drive adoption.
• Document architectures and contribute reusable accelerators. Support presales and proposals.

Must-Have Skills:

• 5 to 8 years in solution engineering, technical consulting, presales, product engineering, or similar customer-facing technical roles.
• Hands-on experience building applications, integrations, or prototypes on AWS.
• Strong grasp of LLMs, RAG, prompt engineering, AI agents, and knowledge retrieval.
• Experience with one or more of Amazon Bedrock, OpenAI, Anthropic, LangChain, LangGraph, or CrewAI.
• Experience delivering customer-facing POCs and running requirements or discovery sessions.
• Strong communication and stakeholder management skills, and comfort with ambiguity.
• Willingness to travel or be deputed to customer sites across India and internationally.

Good to Have:

• AWS Solutions Architect certification (Associate or Professional).
• Multi-agent systems, MCP, and AI observability or evaluation tools.
• Vector databases (OpenSearch, Pinecone, Weaviate, Chroma).
• DevOps and CI/CD exposure.
• Startup, consulting, or SaaS product experience.

Why Join Comprinno:

• Work at the forefront of GenAI, Agentic AI, and AWS.
• High ownership, with solutions going from idea to production in weeks.
• Exposure to diverse industries and to Comprinno's SaaS platform, Tevico.

About Comprinno:

Comprinno is a leading AWS consulting partner specializing in Cloud Transformation, DevOps, Managed Services, Data Analytics, Security, and AI. We help startups and enterprises build scalable, secure, and high-performing cloud environments on AWS.

Learn more about us at: comprinno.net

Roles & Responsibilities

Product Strategy & Discovery

• Identify customer, business, and market opportunities through stakeholder engagement, customer research, discovery workshops, and data analysis.
• Define product vision, roadmap, value proposition, success metrics, and prioritization aligned with business objectives.
• Translate business and customer needs into product requirements, user stories, PRDs, and acceptance criteria.
• Own and continuously prioritize the product backlog based on business value, feasibility, dependencies, risks, and strategic priorities.

AI & Digital Innovation

• Identify and evaluate opportunities to leverage Generative AI, automation, and emerging technologies across products and business processes.
• Define AI-focused use cases, business cases, success metrics, user journeys, and MVP/PoC strategies.
• Collaborate with AI/ML, data, architecture, and engineering teams to convert AI opportunities into scalable product solutions.
• Drive adoption of AI across the product lifecycle and SDLC, including discovery, requirements management, development, testing, analytics, documentation, and support.
• Contribute to enterprise-wide AI initiatives and help scale successful AI PoCs into production-ready solutions.

Presales, Consulting & Solutioning

• Lead or contribute to RFP responses, proposals, client discovery sessions, workshops, and solution presentations.
• Understand client challenges and translate them into digital transformation, modernization, and AI-driven product opportunities.
• Develop solution concepts, use cases, product capabilities, roadmaps, value propositions, and implementation strategies.
• Partner with engineering, UX, architecture, delivery, and commercial teams to define scope, estimates, assumptions, dependencies, risks, and solution feasibility.
• Support client engagements and identify opportunities for account growth through product enhancements, modernization initiatives, and AI adoption.

Product Delivery & Governance

• Partner with UX, engineering, QA, architecture, and delivery teams to ensure successful product execution and delivery.
• Facilitate sprint planning, backlog refinement, UAT/SIT activities, release planning, production validation, and defect prioritization.
• Monitor product KPIs, delivery metrics, adoption, business outcomes, risks, and dependencies.
• Drive stakeholder alignment and continuous improvement throughout the product lifecycle.
• Mentor and guide Product Managers and product team members to strengthen product thinking, decision-making, stakeholder management, and delivery excellence.

Customer & Business Outcomes

• Leverage customer feedback, product analytics, experimentation, and market insights to drive continuous product improvement.
• Lead adoption and successful rollout of new capabilities through effective stakeholder engagement, communication, and change management.
• Ensure products and solutions deliver measurable customer, business, and operational outcomes.

Core Competencies

• Product strategy, discovery, roadmap planning, prioritization, and backlog management.
• Generative AI, AI product thinking, AI use-case identification, and digital innovation.
• Presales, consulting, solutioning, RFP response management, and client engagement.
• Strong business acumen with the ability to connect technology initiatives to business outcomes.
• Excellent communication, presentation, facilitation, and stakeholder management skills.
• Strong understanding of Agile/Scrum methodologies and end-to-end product delivery.
• Ability to collaborate effectively with engineering, architecture, UX, QA, data, and AI/ML teams.
• Strong analytical, problem-solving, and decision-making capabilities.
• Ability to manage ambiguity, multiple workstreams, and senior client stakeholders.

Qualifications

• B.E./B.Tech in Computer Science, Information Technology, Engineering, or an equivalent discipline.
• MBA or certifications in Product Management, Business Analysis, or related fields preferred.
• 10+ years of experience in Product Management, Product Ownership, or Business Analysis.
• Experience working in Agile/Scrum environments with cross-functional technology teams.
• CSPO, PSPO, CBAP, or CCBA certifications are desirable.

Location - India (Remote)

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.
•  

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.

-Turn ambiguous ideas into clear product requirements and workflows.

- Break large ideas into small experiments that can be shipped quickly.

- Communicate customer problems, edge cases, and product intent clearly to engineers and designers.

- Work closely with engineering throughout implementation rather than simply handing over a PRD.

- Test the product yourself and continuously improve it based on actual usage.

### Customers & Market

- Speak directly with customers and prospects.

- Understand how they currently solve problems and where existing tools fail.

- Run product demos and gather structured feedback.

- Separate individual customer requests from broader product opportunities.

- Use customer conversations to continuously refine product strategy.

### Launch & Growth - Own launch strategy for the products and features you build.

- Define positioning, messaging, demos, onboarding, and initial adoption strategy.

- Work across product, engineering, sales, and customers to make launches successful.

- Measure what happened after launch and iterate quickly.

## What We're Looking For

-You likely have **2–4 years of experience** across product, engineering, startups, consulting, AI, or related roles. More importantly, you should have: - **~1 year of hands-on experience building with LLMs, agents, or AI applications.**

- Built actual AI workflows rather than only used ChatGPT or managed an AI project.

- Strong product judgment and ability to simplify complicated problems. - Comfort working with engineers and understanding technical trade-offs.

- Ability to perform deep market and customer research independently. - Strong written and verbal communication.

- High ownership — you naturally move from identifying a problem to getting something shipped.

- Comfort operating in an environment where the answer is often not known beforehand.

## Two Traits We Care About Deeply

### Constraint Thinking Great product people don't start with: *"What could we build?"* They start with: *"Given our users, technology, time, team, data, and constraints, what is the smartest thing we can build?"*

We value people who can reduce a large possibility space into a small number of high-leverage decisions. You should be able to distinguish between: -

interesting vs. useful,

- possible vs. reliable, AI Product Manager 2 / 4 AI Product Manager - demoable vs. production-ready,

- customer requests vs. underlying problems,

- AI capability vs. AI product.

### Grounded Energy AI moves incredibly fast, and it is easy to either become overly skeptical or overly excited. We want someone who has **high energy without hype**. You should be excited enough to constantly experiment with new technology, while grounded enough to recognize its limitations. You should naturally ask:

- Does this actually work?

- How often does it fail?

- What happens at scale?

- Can we evaluate it?

- Will customers actually change their behaviour for this?

- Is AI even necessary here?

## You Might Be a Great Fit If You have independently done things like: - Built an agent over a weekend to test a product idea.

- Compared multiple LLMs for a real use case.

- Created an eval dataset to test AI quality.

- Used tools such as Claude Code, Codex, Cursor, LangGraph, OpenAI Agents SDK, MCP, Langfuse, or similar systems.

- Automated parts of your own product or research workflow with agents.

- Interviewed users and changed the product based on what you learned.

- Gone from idea to prototype to customer feedback to production. - Read product documentation, GitHub repos, Reddit discussions, research papers, and competitor products to understand a market deeply.

## This Role Is Probably Not For You If

- You primarily see PM as backlog management and sprint coordination. - You need detailed requirements before you can start working.

- You prefer delegating prototyping and experimentation entirely to engineering.

- You haven't spent meaningful time building with modern AI tools.

- You enjoy creating large strategy documents more than testing ideas with users.

- You are uncomfortable making decisions with incomplete information.

## What Success Looks Like Within your first few months, you should be able to independently: AI Product Manager 3 / 4 AI Product Manager - understand a customer problem,

- research the market,

- identify a promising product opportunity,

- prototype an AI workflow,

- define how its quality should be evaluated,

- work with engineering to productionize it,

- speak with early users,

- launch it,

- measure adoption,

- and recommend what we should do next.

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 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.