AI Solutions Engineer

About MyOperator

MyOperator is a Business AI Operator, a category-leader that unifies WhatsApp, Calls, and AI-powered chat & voice bots into one intelligent business communication platform. Unlike fragmented communication tools, MyOperator combines automation, intelligence, and workflow integration to help businesses run WhatsApp campaigns, manage calls, deploy AI chatbots, and track performance — all from a single, no-code platform. Trusted by 12,000+ brands including Amazon, Domino's, Apollo, and Razorpay, MyOperator enables faster responses, higher resolution rates, and scalable customer engagement — without fragmented tools or increased headcount

Role Summary

We’re hiring a Front Deployed Engineer (FDE)—a customer-facing, field-deployed engineer who owns the end-to-end delivery of AI bots/agents.

This role is “frontline”: you’ll work directly with customers (often onsite), translate business reality into bot workflows, do prompt engineering + knowledge grounding, ship deployments, and iterate until it works reliably in production.

Think: solutions engineer + implementation engineer + prompt engineer, with a strong bias for execution.

Responsibilities-

Requirement Discovery & Stakeholder Interaction

• Join customer calls alongside Sales and Revenue teams.
• Ask targeted questions to understand business objectives, user journeys, automation expectations, and edge cases.
• Identify data sources (CRM, APIs, Excel, SharePoint, etc.) required for the solution.
• Act as the AI subject-matter expert during client discussions.

Use Case & Solution Documentation

• Convert discussions into clear, structured use case documents, including:
• Problem statement & goals.
• Current vs. proposed conversational flows.
• Chatbot conversation logic, integrations, and dependencies.
• Assumptions, limitations, and success criteria.

Customer Delivery Ownership

• Own deployment of AI bots for customer use-cases (lead qualification, support, booking, etc.). Run workshops to capture processes, FAQs, edge cases, and success metrics. Drive the go-live process: requirements through monitoring and improvement.

Prompt Engineering & Conversation Design

• Craft prompts, tool instructions, guardrails, fallbacks, and escalation policies for stable behavior. Build structured conversational flows: intents, entities, routing, handoff, and compliant responses. Create reusable prompt patterns and "prompt packs."

Testing, Debugging & Iteration

• Analyze logs to find failure modes (misclassification, hallucination, poor handling). Create test sets ("golden conversations"), run regressions, and measure improvements. Coordinate with Product/Engineering for platform needs.

Integrations & Technical Coordination

• Integrate bots with APIs/webhooks (CRM, ticketing, internal tools) to complete workflows. Troubleshoot production issues and coordinate fixes/root-cause analysis.

What Success Looks Like

• Customer bots go live quickly and show high containment + high task completion with low escalation.
• You can diagnose failures from transcripts/logs and fix them with prompt/workflow/knowledge changes.
• Customers trust you as the “AI delivery owner”—clear communication, realistic timelines, crisp execution.

Requirements (Must Have)

• 2–5 years in customer-facing delivery roles: implementation, solutions engineering, customer success engineering, or similar.
• Hands-on comfort with LLMs and prompt engineering (structured outputs, guardrails, tool use, iteration).
• Strong communication: workshops, requirement capture, crisp documentation, stakeholder management.
• Technical fluency: APIs/webhooks concepts, JSON, debugging logs, basic integration troubleshooting.
• Willingness to be front deployed (customer calls/visits as needed).

Good to Have (Nice to Have)

• Experience with chatbots/voicebots, IVR, WhatsApp automation, conversational AI platforms with at least a couple of projects. 
• Understanding of metrics like containment, resolution rate, response latency, CSAT drivers.
• Prior SaaS onboarding/delivery experience in mid-market or enterprises.

Working Style & Traits We Value

• High agency: you don’t wait for perfect specs—you create clarity and ship.
• Customer empathy + engineering discipline.
• Strong bias for iteration: deploy → learn → improve.
• Calm under ambiguity (real customer environments are chaotic by default).

The role

A working product and a working deployment are two different things. You are the person who closes that gap.

You sit inside the client's head office. You get the deployment live, you get their teams using the dashboards, and you own whether the AI is returning something worth acting on. Every client is different: different languages on the floor, different store noise, different vocabulary for the same product, different CRM, different idea of what a good conversation looks like. The core platform does not change for each of them. You are the layer that makes it fit, and you are the one the client meets.

You are encouraged to spend time in stores. The engineers who do the best work here are the ones who have stood on a shop floor and watched where the pitch and the pipeline actually break. Nobody will make you go. You will also spend real time in the codebase, because you fix what you find rather than filing it.

What you build has a commercial edge to it. A pilot converts when the client sees the result they were promised, and an account grows when a second team inside it sees what is already sitting in their data. Both of those outcomes are yours to deliver, not somebody else's to chase.

How you will work

You design the deployment, you build it, and you own whether it holds up on a Saturday evening in a crowded store. Nobody hands you the plan, and nobody hands you the spec. You write both.

The decisions are yours. Which integration is worth the week, which vertical taxonomy needs building, what ships in the pilot and what waits, and when to tell a client that the thing they are asking for is the wrong thing to build. You go and find out what a client needs before anyone writes a line of code.

You will not be doing it alone. There are founders, AI engineers and product people around you, and they will build alongside you. What nobody will do is tell you what the client needs. That call is yours.

The work compounds if you do it well. What you learn on one deployment becomes a specification, then code, then a pattern the next one starts from. A year in, the deployments you designed should be running without you, and a new client should take a fraction of the time the first one did.

What you will do

Own the deployment end to end. Device provisioning, store connectivity, data flowing, first insight in front of the client. Get from kickoff to something real inside

the pilot window, and know by the halfway mark whether it is in trouble.

Get their HQ using it. A dashboard nobody opens is a failed deployment. Sit with the sales, marketing and L&D teams, show them what is in their own data, and

make sure the people who asked for this are actually looking at it every week.

Make the AI work on their floor. Their languages, their store noise, their product vocabulary. Benchmark transcription and speaker separation on their actual

audio, and fix what fails instead of explaining it away.

Build the vertical. Intent taxonomies, objection maps and prompt libraries for the category you are deployed into. A jewellery floor and an electronics floor do not

share a conversation model.

Wire it into their systems. CRM and POS integrations, so conversation data connects to what actually got sold and the insight can be checked against reality.

Build what the client asks for. Custom reports, dashboards and agents. Ground everything in source conversations and verify it before it ships, because a confident

wrong number costs an account.

Close the pilot. A pilot converts on results, not on effort. Know what the client agreed to judge this on, work backwards from it, and make sure the output in front of

their leadership at the end is the thing they asked for.

Grow the account. The same intelligence is worth something to marketing, L&D and category teams inside the same client. Spot which of them would benefit, show them what is already in their data, and hand a real opening to the account team.

Push it back into the product. Turn one-off client work into something the platform does by default, so the next deployment starts further ahead than this one did.

What we are looking for

Must have

• 0 to 5 years of experience. A consulting internship or an analyst role is the closest match to what this job actually asks for, but we care more about what you can do than where you did it
• Coding ability, ideally Python. Degree, internship, first job or your own projects. What we want to see is something you built that other people actually used
• Excel or Sheets at a real working level. A lot of the first conversation with a client happens in a spreadsheet before it ever happens in a dashboard
• The ability to explain a complicated idea simply. You will be taking AI output to people who do not think about models, and the explanation matters as much as the result
• Comfort at the boundaries. APIs, data pipelines, some frontend, some hardware when a device misbehaves
• An eye for where a deployment turns into more business, and the willingness to raise it yourself
• Heavy hands-on LLM usage. Prompts, evaluations, retrieval, and a clear view on where these tools break
• Fluent English and Hindi. A third Indian language counts for a lot, since the useful conversations happen on store floors and not only in HQ meeting rooms
• The instinct to go and find out what a client needs rather than waiting to be told

Good to have

Speech or audio work. Transcription, diarization, voice activity detection, or

anything that survives noisy real-world recording

Embedded or IoT experience, on ESP32 or similar

SQL and experience building things customers actually look at

Side projects, hackathons or internships where you shipped without a spec

and it worked

Forward Deployed Engineer (FDE)

About the role

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

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

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

What you will own

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

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

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

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

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

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

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

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

What "AI-native" means here

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

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

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

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

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

Technical baseline

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

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

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

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

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

How you work

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

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

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

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

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

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

Nice to have

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

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

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

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

such as SOC 2.

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

How we evaluate

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

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

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

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

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

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

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

Role details

Title : Forward Deployed Engineer (FDE)

Employment type : Full-time employee

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

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

years

Team :Part of a project team, working alongside a

product or account lead

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

American clients

Domain : Generalist, across industries

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

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

nine three one six one two zero one three two

About Asha Health

Asha Health helps medical practices launch their own AI clinics. We're backed by Y Combinator, General Catalyst, 186 Ventures, Reach Capital and many more. We recently raised an oversubscribed seed round from some of the best investors in Silicon Valley. Our team includes AI product leaders from companies like Google, physician executives from major health systems, and more.

About the Role

We're looking for a top 0.01% Software Engineer to join our engineering team in our Bangalore office.

4.6 fundamentally changed the game, which means that high intelligence, high agency engineers can now do the work of 10+ good engineers. It doesn't make sense to have anyone but the best on the team.

Since low level coding has become easier, what we expect from engineers on our team has expanded. Engineers on our team are expected to:

• Ship features end to end at a rapid pace
• Deeply research the domain and be their own product managers
• Ensure reliability and quality is best-in-class
• Design robust eng architecture, and develop testing and observability tools for each feature pre-launch
• Build each feature with deep customer empathy, meaning planning out and building stellar UX yourself

This means to thrive in a startup environment like ours, you not only need to be a stellar engineer, but you need to:

1. Be super adept with AI development tools and building the AI systems that build your features for you (Conductor, Browser agents, QA agents, Ralph loops, adverserial agents, and more).
2. Have exceptional product and UX taste, meaning you can ship features that are more effective than those historically designed by teams of product managers and designers.
3. Take the highest level of ownership around feature outcomes, reliability, and observability.

Other Points to Note

1. We are growing rapidly, our work has impact on tens of thousands of patients if not more.
2. On our team, everything you do is on the bleeding edge of applied AI.
3. We expect a high level of commitment from everyone on the team, most folks work 6 days and lead every project with intensity. It's a high ask, and we only bring on the best people. We compensate significantly above market, accordingly.

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

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.

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.

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.

Product Engineer — Role Summary

We are looking for a Product Engineer to build user-facing products that combine AI capabilities with practical applications. You will work at the intersection of software engineering and AI, developing autonomous, agent-driven systems that solve complex educational and research problems.

Key Responsibilities:

• Product Development: Design, build, and deploy production-ready applications powered by LLMs and AI agents.
• Data Engineering: Build scalable ETL/ELT pipelines to process structured and unstructured data, including text and audio, for RAG and model fine-tuning.
• Agentic Workflows: Develop multi-step AI agents with tool calling, APIs, databases, search, reasoning, and memory.
• Rapid Prototyping: Turn ideas and research concepts into interactive, production-ready applications.
• AI Integration: Use frameworks such as LangChain, LlamaIndex, AutoGen, or custom orchestrators to integrate AI into scalable systems.
• User Experience: Transform raw AI outputs into reliable, intuitive, and responsive user experiences.
• Collaboration: Work closely with ML researchers and data engineers to integrate custom and fine-tuned models.
• Observability: Monitor agent behavior, manage edge cases, reduce hallucinations, and improve reliability in production.

The ideal candidate combines strong software engineering, AI/LLM expertise, data engineering, and product thinking, with the ability to take an AI concept from prototype to production.

About Juicelabs

Where creative engineering meets applied AI.

We build vertical, AI-native tools that unlock fresh insights and creative superpowers for clients in advertising, ecommerce, and beyond. We're a nimble team of founders and builders obsessed with using AI to solve real problems in ways that didn't exist eighteen months ago.

The Opportunity

A year ago, we would have posted this as a backend role. That role doesn't really exist any more.

Code has become nearly invisible. Agents write it, test it, and iterate on it faster than anyone could read it. What's left is the work above it: deciding what's worth building, designing systems that hold up at 10x, and building the setup that turns a clear spec into working software.

That's the job. At your level, it isn't one system. It's several workstreams, running at once.

You'll work directly with our Cofounder/CTO on products going from zero to one, with AI at the core of both what we build and how we build it. Real ownership, real scope, and an unusual amount of leverage: one engineer with good judgment and a well-tuned agent setup now ships what a small team used to.

What you'll do

• Own multiple workstreams in parallel. Two or three features of the same product moving at once, each with its own scope, edge cases, and failure modes. You decide where your attention goes this week, and nothing goes stale.
• Run a fleet, not a single agent. Design the setup that makes high-throughput work possible: how work gets broken down, specced, and run across many agents at once, so output scales without things breaking.
• Design the system. Service boundaries, data models, failure modes. Make the architectural calls before a line gets generated, and be able to explain the trade-off six months later.
• Write the spec. PRDs and technical designs are the new source code. A vague spec produces confidently wrong software, fast. Scope, edge cases, non-goals, what "done" means.
• Own it end to end. Ideation → design → build → deploy → monitor → iterate, including functional UX. Nobody hands you tickets.
• Set the workflow. Bring your harness: CLAUDE.md, AGENTS.md, subagents, custom commands, eval loops — whatever makes agents reliable rather than merely impressive. We want to steal your good ideas.

What we're looking for

• High agency. You see the gap and close it. You don't wait for direction, and you don't need to be chased for follow-through. This is the single trait we screen hardest for. And you can do it across several workstreams at once, without dropping threads.
• Real system design depth. You can reason about consistency, caching, queues, and scale — and about complexity, because you need to know when an elegant-looking solution is quietly quadratic. LeetCode ninja not required. Being able to explain why this won't blow up at 10x, very much required.
• Clear thinking, put to paper. You get the thinking straight first, then write it down so nobody has to guess: PRDs, design docs, PR comments. Writing is most of how you'll direct both humans and models, and muddy thinking on paper becomes muddy software.
• You've shipped to production. Deployed things, monitored them, been paged by them, fixed them. Docker, CI/CD, cloud infra. Our stack is mostly Python (FastAPI), Postgres, GCP/AWS — but we care more about how you think than which framework you've memorised.
• You orchestrate AI, not just use it. Not "I've tried Copilot," and not just "I use Claude Code." You've run multiple agents in parallel and developed opinions: how to split work so they don't step on each other, where they excel, where they confidently lie, how you structure a repo so they don't wander, and how you verify what comes back at volume.
• Startup energy. Multiple hats, fast cycles, and the judgment to balance "ship it now" against "will this survive contact with real users?"

Experience

3–6 years of professional experience (full-time positions, excluding internships).

Location / How to apply

On-site at our office in HSR Layout, Bangalore.

Apply through this posting, or email us with a link to the best thing you've built.