AWS Delivery Lead

AI Architect – Enterprise AI & Generative AI ( Remote) - Contractual

Experience: 8+ years

Location: Remote, India

Employment Type: Contractual

Working Hours: 3:00 PM – 12:00 AM IST

Client Engagement: US-based client

About the Role

We are looking for a hands-on, client-facing AI Architect to lead the architecture, technical direction, and production implementation of enterprise AI and Generative AI solutions for a US-based client.

You should combine strong software engineering and cloud architecture fundamentals with practical experience building secure, scalable, and reliable AI applications. This role involves end-to-end technical ownership, from solution design and prototyping to production deployment and optimization. It is not a research-only, advisory-only, or prompt-writing position.

Key Responsibilities

• Lead end-to-end architecture and delivery of AI/ML and Generative AI solutions, from discovery and proof of concept to production rollout.
• Design LLM-powered applications using Retrieval-Augmented Generation (RAG), embeddings, vector databases, AI agents, and orchestration frameworks.
• Define data ingestion, chunking, retrieval, grounding, evaluation, and knowledge-refresh strategies.
• Select appropriate AI models and platforms based on quality, latency, cost, security, and operational requirements.
• Design cloud-native architectures, APIs, integrations, identity controls, monitoring, and resilient deployment patterns.
• Create architecture diagrams, technical designs, implementation roadmaps, and technical standards.
• Implement AI quality and safety measures, including hallucination mitigation, guardrails, access controls, and prompt-injection defenses.
• Guide engineering, data, ML, and DevOps teams through implementation, code reviews, troubleshooting, and go-live readiness.
• Lead technical discussions and design reviews with US client stakeholders.
• Establish production monitoring, CI/CD, versioning, incident handling, and performance and cost optimization practices.

Required Skills & Qualifications

• 8+ years of relevant experience in software engineering, AI/ML engineering, data engineering, or technology architecture.
• Proven experience personally architecting and deploying at least one enterprise-grade AI or Generative AI solution into production.
• Strong practical knowledge of LLMs, RAG, embeddings, vector databases, orchestration frameworks, and model evaluation.
• Hands-on experience with Python and APIs, including prototyping, code review, and troubleshooting.
• Experience with at least one major cloud platform: Microsoft Azure, AWS, or Google Cloud Platform (GCP).
• Understanding of data pipelines, SQL/NoSQL databases, containers, CI/CD, MLOps, and LLMOps.
• Knowledge of enterprise security, privacy, identity and access management, and responsible AI practices.
• Experience independently leading architecture workshops, design reviews, and technical discussions with US clients.
• Excellent written and spoken English, documentation, and stakeholder management skills.
• Availability to work from 3:00 PM to 12:00 AM IST and willingness to accept a contractual engagement.

Preferred Experience

• Azure AI Foundry, Azure OpenAI, Amazon Bedrock, or Google Vertex AI.
• Azure AI Search or equivalent retrieval tools.
• LangChain, LangGraph, Semantic Kernel, and agent/tool orchestration.
• Model fine-tuning, predictive ML, document intelligence, multimodal AI, or real-time AI applications.
• Experience in cloud modernization, enterprise integrations, data analytics, or technology consulting.
• Relevant AI/cloud architecture certifications and experience mentoring technical teams.

What Success Looks Like

• Designing secure, scalable, production-ready AI solutions rather than demos or conceptual presentations.
• Making sound technical decisions around feasibility, performance, reliability, cost, and AI risks.
• Taking ownership of architecture decisions and resolving technical blockers with limited supervision.
• Building trust with senior US stakeholders and collaborating effectively with India-based delivery teams.

Contract Details

• Initial engagement: Minimum 3 months, with extension subject to project requirements and performance.
• Working schedule: 3:00 PM – 12:00 AM IST.
• Potential permanent employment: After 18 months, the client may consider direct permanent employment at its sole discretion. Conversion is not guaranteed.

Mandatory Screening Criteria

Candidates should be able to:

• Explain an enterprise AI/GenAI solution they personally architected and deployed, including architecture, scale, evaluation, and business impact.
• Whiteboard an enterprise RAG or agentic solution covering data flow, security, authentication, authorization, monitoring, and cost controls.
• Discuss real-world model-quality issues, hallucinations, privacy risks, incidents, and architectural trade-offs.
• Lead a technical design review with US client stakeholders.
• Confirm acceptance of the working hours and contractual engagement.

Apply only if you have hands-on experience architecting and deploying enterprise AI/GenAI solutions in production.

Role Summary 

The GCP Delivery Lead is responsible for driving the technical vision, architecture, and delivery of enterprise-scale solutions on Google Cloud Platform (GCP). This role combines deep technical expertise with strong delivery leadership and customer engagement capabilities, working closely with clients, Google teams, and internal stakeholders. 

Key Responsibilities 

Technical Leadership 

• Define and own GCP solution architecture and technical strategy for client engagements 
• Design scalable, secure, and resilient cloud-native architectures 
• Establish architecture standards, best practices, and reusable frameworks 
• Provide governance and oversight across multiple programs 

Delivery Leadership 

• Ensure successful delivery of cloud transformation and modernization initiatives 
• Conduct architecture reviews, risk assessments, and quality assurance 
• Guide engineering teams through complex implementations 
• Ensure solutions meet performance, security, and scalability standards 

Google Partnership & Pre-Sales 

• Act as primary technical interface with Google Cloud teams 
• Support joint solutioning, co-selling, and client workshops 
• Contribute to proposals, solution design, and executive presentations 

AI & Data Solutions 

• Lead enterprise AI, GenAI, and data platform architecture on GCP 
• Drive adoption of Vertex AI, BigQuery, and modern AI/ML platforms 
• Advise clients on AI strategy, transformation, and innovation 

Practice Development 

• Build and mentor high-performing architecture and engineering teams 
• Drive certifications, capability building, and hiring initiatives 
• Create accelerators, reusable assets, and reference architectures 
• Contribute to practice growth and go-to-market strategies 

Required Qualifications 

Experience 

• 12+ years in cloud architecture, software engineering, or consulting 
• 5+ years of hands-on GCP architecture and delivery experience 
• Proven track record in large-scale cloud transformation programs 
• Experience managing multi-client and multi-program delivery 

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.

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

Technical Architect – Product Engineering

Experience: 15+ Years

Location: Pune, India

Employment Type: Full-time

Desired Skills: Python, Technical Architecture, AWS, Microservices, SaaS / Multi-tenant Architecture, Kubernetes, System Design

About the Role

We are looking for a Senior Technical Architect to lead the architecture, design, and technical evolution of an enterprise SaaS product. This is a hands-on leadership role requiring deep technical expertise, strong product engineering experience, and the ability to build scalable, secure, and high-performance platforms.

The ideal candidate should be passionate about solving complex engineering problems, driving innovation, mentoring development teams, and effectively leveraging AI to accelerate software development.

Key Responsibilities

• Own the overall product architecture and technical roadmap.
• Design and build scalable, secure, and highly available enterprise applications.
• Lead the design and implementation of new product features from concept to production.
• Remain hands-on with coding and contribute to critical product components.
• Drive architecture reviews, code quality, performance optimization, and engineering best practices.
• Lead cloud architecture, security, scalability, and DevOps initiatives.
• Evaluate and adopt modern technologies to improve product capabilities and engineering efficiency.
• Leverage AI tools (ChatGPT, GitHub Copilot, Cursor, Claude, etc.) to accelerate software development, code reviews, testing, documentation, debugging, and productivity.

Required Skills & Qualifications

• 15+ years of software product engineering experience with at least 5 years in a Technical Architect role.
• Strong hands-on expertise in Python and modern backend frameworks.
• Deep experience with AWS services and cloud-native application architecture.
• Strong understanding of DevOps, CI/CD pipelines, Infrastructure as Code (Terraform/CloudFormation), Docker, Kubernetes, and container orchestration.
• Experience designing microservices, REST APIs, event-driven architectures, and distributed systems.
• Strong knowledge of SQL and NoSQL databases.
• Experience with scalable SaaS platforms, multi-tenant architectures, and secure application design.
• Excellent understanding of software design patterns, performance tuning, observability, and system reliability.
• Strong analytical, problem-solving, and decision-making skills.

Read This Before Anything Else

We have 6 developers who can ship. What we don't have is someone who turns that into a real engineering function: real architecture, real leverage, real AI-driven advantage. If that gap sounds like an opportunity rather than a headache, you're in the right place. If it sounds like a lot of undefined work with no playbook handed to you, this one probably isn't for you. That's completely okay. There are plenty of great roles that fit differently.

About CraftMyPlate

CraftMyPlate is Hyderabad's go-to platform for food experiences for micro-events: house parties, birthdays, office celebrations, festive gatherings, and more. We're building the operating system for how India discovers, customises, and orders food for smaller events. We're backed by established founders and investors, and we're funded and growing fast. The next phase of that growth runs through engineering.

Where We Stand

Some numbers, because they matter more than adjectives. Order volume has grown 50x in two years, and we're compounding at roughly 3x year over year, without giving up equity to fund it. That means the business runs on its own economics. The growth is real demand, not runway bought with dilution, and every efficient architectural decision this role makes directly protects that.

Most people size up an opportunity by asking what's going to change in ten years. The more useful question, and the one this company is built around, is what won't change. People will keep gathering. They'll keep celebrating, hosting, and marking festivals, in 10 years and in 20. That permanence is the bet. You're not building infrastructure for a trend cycle. You're building for a category that outlasts the current AI wave, the next funding round, and probably us too.

The Technical Reality

Here's an honest read of the engineering problem, not a sanitized version of it.

Event-driven commerce doesn't scale like typical e-commerce. Demand isn't smooth, it's spiky: weekends, festival calendars, and event dates create real load concentration, and each order is tied to a hard deadline that can't slip the way a shipped package can. That has direct architectural consequences: systems need to handle bursty, unpredictable traffic without paying for idle capacity the rest of the time, which is exactly why we're serverless-first on AWS rather than running a fixed fleet sized for peak.

Underneath that, every order touches multiple systems that have to stay consistent: kitchen and vendor fulfillment status, inventory across partners, payment gateway settlement, and refunds, often in real time and often across more than one vendor for a single event. Getting that consistency right across SQL and NoSQL stores, without it becoming a source of support tickets and manual reconciliation, is a real architecture problem, not a CRUD problem.

The AI-agent layer is the next lever, and it's a business lever as much as a technical one. Every workflow we can hand to a well-orchestrated agent instead of a new hire is a workflow that scales without adding headcount, which is exactly how a company grows 3x a year without diluting equity to fund the team behind it. That's why agent orchestration across multiple LLMs, using LangGraph, sits in the "go deep" tier of this role rather than being a nice-to-have.

You'll likely find some of this framing right and some of it worth challenging once you're actually in the codebase. That's expected, and honestly preferred over someone who just nods along.

Why This Role Exists

You'll be the most senior technical person in the company, reporting directly to the founder. Not a manager brought in to run standups. An owner. You set the architecture, you write code yourself, and you make the team materially better. You also own where AI and automation take this company next, starting with our first in-house AI agent product (details shared in the interview), and expanding from there into how the company runs, department by department: HR, finance, marketing, design, development, all sitting on an engineering layer that you design.

If you've outgrown a role where you plan but don't build, or where good ideas die in a committee, this is built to be the opposite of that.

What You'll Own

• Architecture, end to end. Scalable, cost-efficient systems from day one, not "fix it later" engineering. You own the decisions and their long-term consequences.
• Hands-on building. You are still writing code and shipping. This isn't a seat where you review other people's work all day. You lead by building.
• The engineering team. Directly manage, mentor, and level up our 6 developers. Build the technical bar, the review culture, and the calibration that lets the team ship independently.
• The AI-agent roadmap. Own the architecture behind our first AI agent product, then the broader strategy for AI agents and automation across every function in the company, with engineering as the layer underneath all of it.
• Team scaling. Build the next layer of leads under you so execution quality scales without you being the bottleneck.
• Technical accountability. When something breaks, you fix it. You don't escalate and wait.

Our Stack, and the Depth We Expect

Not everything on this list needs the same level of mastery. Some of it you need to own at an architectural level. The rest you need to be strong enough to build yourself, direct the team on, or delegate to AI agents with confidence.

Go deep here. This is where the real architecture decisions live, and where the business impact is highest:

• AWS, serverless first. You should be genuinely well versed in AWS application development, not just "have used AWS." You should be able to design and guide serverless architecture (Lambda, API Gateway, DynamoDB, Step Functions, and similar) as our default way of building, because our demand curve is spiky by nature and fixed infrastructure is money left on the table.
• TypeScript, our primary language across backend and frontend.
• Agent orchestration across multiple LLMs, using LangGraph. This is core to our AI roadmap and our path to scaling operations without scaling headcount. You own how it's architected, not just how it's used.

Working proficiency. Build it yourself, direct the team, or hand it to an AI agent and know if the output is right.

This Is You If

• You've built and shipped real production systems yourself, not just reviewed other people's architecture from a distance.
• You go deep wherever the problem is, and you're comfortable owning the exact stack described above, not just "full-stack" in the abstract.
• You've made engineers around you measurably better, whether or not you've held the title for it yet.
• You're already using AI coding tools and agents seriously, like Claude, Cursor, or similar tools, as part of how you build, not as something you tried once. We'll likely explore this together in the interview.
• You have a bias toward leverage over hours. You'd rather automate or systematize a problem than grind through it. But when something's live and needs to be done right, you see it through completely, with no half-finished work.
• You want to build something for years, not land somewhere comfortable. We'll know the difference from how you talk about your last three years.

This Might Not Be the Right Fit If

• You'd prefer a stable, well-defined role with clear boundaries and someone else making the calls. That's a fair thing to want, just not what this is.
• You'd rather receive direction than bring us architecture and AI strategy yourself.
• You haven't yet gotten hands-on with AI coding tools in your daily work.
• You're drawn more to the title than the work behind it.

If none of that sounds like you, we'd love to hear from you.

Requirements

• 5 to 7 years of experience in software engineering, with real ownership of architecture-level decisions, not just feature delivery.
• Prior experience leading or mentoring engineers, formally or informally.
• Tier-1 or Tier-1+ engineering college strongly preferred (IIT, BITS, top NIT tier, or equivalent). We'll consider other institutions only with clearly commendable, verifiable work: real systems you can walk us through in depth, strong open-source contributions, or a track record that speaks for itself. Pedigree is a proxy for speed, not a checkbox. We test for the underlying ability regardless.
• Comfortable in an early-stage environment: undefined problems, few processes, and the expectation that you help define both.

Compensation

Competitive, with equity. We're formalizing a structured ESOP program alongside this hire. Specific numbers are discussed directly in later interview rounds.

If reading this got you a little excited about what you'd build here, we'd genuinely love to talk. If it didn't quite land, no hard feelings. We just want the right fit for both sides.

Position Summary

We are looking for an experienced Enterprise Architect to define and deliver enterprise-scale digital experience architectures leveraging AI/ML capabilities, data platforms, and modern cloud technologies. The role involves partnering with senior business and technology stakeholders to translate business objectives into scalable architectures, strategic roadmaps, and technology solutions.

The ideal candidate will have strong expertise in enterprise architecture, cloud-native technologies, AI-driven customer experiences, and digital marketing platforms, along with the ability to lead architecture initiatives across cross-functional teams.

Key Responsibilities

• Enterprise Strategy & Roadmaps: Define digital, data, and AI strategies, translating business vision into actionable architecture roadmaps aligned with business goals and KPIs.
• Architecture Design: Design end-to-end enterprise and solution architectures integrating digital experience platforms, customer data platforms, AI/ML capabilities, and cloud services.
• Cloud & Technology Architecture: Recommend scalable, secure, and resilient architectures using cloud-native, API-first, microservices, event-driven, and serverless principles across Azure, AWS, or other public cloud platforms.
• AI & Data Architecture: Define architectures for customer data platforms, identity and profile management, data ingestion, governance, personalization, journey orchestration, and AI-powered experiences.
• Architecture Governance: Establish architecture standards, reference architectures, governance frameworks, and operating models to ensure consistency, security, and sustainable technology adoption.
• Stakeholder Management: Collaborate with business leaders, engineering teams, data architects, developers, and data scientists to drive architecture alignment and execution.
• Executive Communication: Lead architecture workshops, visioning sessions, and roadmap presentations, communicating technical recommendations and trade-offs to senior leadership.
• Practice Development: Contribute to reusable architecture assets, mentor architects and consultants, and support presales activities, solutioning, estimations, and proposals.

Required Skills & Experience

• Proven experience in enterprise architecture, solution architecture, and developing enterprise architecture models and roadmaps.
• Strong understanding of architecture frameworks and principles, including TOGAF, SOA, and API-led architecture.
• Hands-on experience with public cloud architecture and services, particularly Microsoft Azure and/or AWS.
• Experience designing and integrating AI-powered solutions, with knowledge of LLMs, prompt engineering, RAG, and model evaluation.
• Understanding of agentic workflows, tool-using agents, and enterprise AI governance, observability, and reliability.
• Experience working with software engineers, application developers, data architects, and data science/ML teams to deliver end-to-end solutions.
• Ability to translate business requirements and strategic vision into technical architectures and implementation roadmaps.
• Strong stakeholder management, leadership, presentation, and communication skills, including experience engaging with senior executives.
• Ability to lead architecture execution across multidisciplinary teams and communicate complex technical concepts to diverse audiences.

Good to Have

• Experience with Adobe Experience Cloud, Adobe Experience Platform, or one or more digital experience solutions.
• Previous experience with other digital marketing and customer experience platforms.
• Experience with customer data platforms, personalization, omnichannel engagement, and marketing optimization initiatives.
• Experience supporting consulting engagements, presales, and enterprise transformation initiatives.

Additional Requirement

- Willingness to travel up to 50%, depending on business and client requirements.

Job Title: BDM – AWS Cloud Sales (Business Development / Sales Manager/DM – AWS/Cloud)

Location: Andheri E, Mumbai

Schedule: Mon to Fri, Onsite

Experience: 6-10 years

Key Requirements

• Proven experience in AWS Cloud sales/business development.

• Strong understanding of AWS services and cloud adoption journeys.

• AWS Cloud Practitioner certification (minimum) is mandatory.

• Good knowledge of AWS programs such as MAP, Funding Programs, WAFR, POC Credits, ISV/Startup programs, etc.

• Established working relationships with AWS Account Managers, Partner Development Managers, and the AWS ecosystem.

• Excellent communication, presentation, and stakeholder management skills.

• Strong client-facing and consultative selling capabilities.

• Good understanding of AWS AI/ML and Generative AI services (Amazon Bedrock, Amazon Q, SageMaker, etc.) and the ability to position AI-led solutions.

• Experience in solution-based selling in collaboration with pre-sales and technical teams.

• Self-driven individual with a strong network and proven track record of achieving sales targets.

Note: Prior cloud reseller experience is not mandatory. We are primarily looking for candidates with strong AWS cloud selling experience and a good understanding of the AWS partner ecosystem.

About the Role

We are looking for a Solution Architect with 10–15 years of experience to design scalable, secure, and cloud-ready solutions. The role involves working with customers, Senior Solution Architects, business analysts, project managers, and engineering teams across cloud, modernization, microservices, integration, and digital transformation initiatives.

Must Have Skills

• 10–15 years of experience in software development, architecture, and enterprise application delivery.
• Strong experience with Azure and/or AWS and cloud-native architecture.
• Hands-on experience with Microservices, REST APIs, SOA, and system integration.
• Experience in application modernization and cloud migration.
• Knowledge of Docker, Kubernetes, CI/CD, and Infrastructure as Code.
• Understanding of cloud security, IAM, networking, monitoring, and disaster recovery.
• Ability to create architecture diagrams, solution designs, and technical documentation.
• Strong analytical, communication, and customer-facing skills.

Good To Have Skills

• AWS/Azure certifications.
• Experience with Serverless, Event-Driven Architecture, Messaging, and API Management.
• Exposure to SaaS / Multi-Tenant Architecture.
• Knowledge of Generative AI and AI-enabled solutions.
• Experience with pre-sales, POCs, estimations, and technical proposals.
• Exposure to application security and compliance.
• Experience working with global customers and distributed teams.

Responsibilities

• Design end-to-end solutions and independently own defined architecture workstreams.
• Translate business and technical requirements into application, cloud, integration, and deployment designs.
• Define microservice, API, integration, and data-flow architectures.
• Guide engineering teams on architecture implementation and conduct design/code reviews.
• Contribute to cloud migration, modernization, and digital transformation initiatives.
• Participate in customer workshops, technical discussions, POCs, and solution demonstrations.
• Identify and address architectural, performance, security, and technical risks.
• Ensure solutions follow defined architecture, security, and engineering standards.
• Work closely with the Senior Solution Architect on complex and strategic engagements.
• Develop reusable architecture patterns, templates, and technical accelerators

Next

About the Company

As the world moves toward AI-native applications, one element matters more than anything: data.

We are building a platform that helps organizations unlock the value hidden within customer feedback. By centralizing data from multiple sources—including CRM platforms, support systems, collaboration tools, surveys, community forums, and customer conversations—into a unified intelligence layer, we enable teams to understand customer needs and make better product decisions.

Built on top of this foundation is an AI-powered platform that allows teams to query, monitor, and act on customer intelligence directly within their workflows. Our customers include some of the fastest-growing product-led technology companies as well as large global enterprises.

Backed by leading global venture capital firms, our engineering culture is AI-native from the ground up. Agents, LLMs, and applied machine learning are not side projects—they are central to how we build products. We operate at the cutting edge of agentic systems and continuously push the boundaries of what modern AI applications can achieve.

What You Will Do

• Convert product direction into phased technical plans, including estimates, milestones, dependencies, and risk assessments.
• Make thoughtful trade-offs across correctness, latency, user experience, AI/model behavior, reliability, cost, and delivery speed.
• Define clear ownership boundaries across frontend applications, BFF layers, backend services, agent workflows, ML platforms, and data contracts.
• Review ERDs, PRDs, design documents, and implementation plans to identify gaps before they become execution challenges.
• Drive planning and execution for complex initiatives while empowering area leads and senior engineers to maintain ownership.
• Partner closely with Product, Design, Engineering, and GTM teams to turn ambiguous problem statements into executable solutions.
• Influence technical direction through strong engineering judgment, architectural leadership, and hands-on involvement.
• Mentor senior engineers and contribute to engineering excellence through reviews, incident analysis, and proactive risk management.

What It Takes

• 9+ years of experience as a Senior Engineer, Technical Lead, or equivalent role.
• Demonstrated experience owning architecture and execution for large-scale product or platform initiatives spanning multiple teams.
• Strong foundation in distributed systems and modern software architecture.
• Exposure to agentic AI systems, machine learning engineering, MLOps platforms, and frontend/BFF architectures.
• Ability to own end-to-end product and technical problems rather than specializing exclusively in a single layer of the stack.
• Proven track record of converting ambiguity into actionable engineering plans.
• Excellent written communication skills, including design documentation, architectural reviews, trade-off analyses, and RCA documentation.
• Ability to challenge requirements constructively and collaborate effectively across Product, Design, Engineering, and GTM functions.
• High ownership mindset, attention to detail, and a low-ego, collaborative approach.