AI/ML Architect

An AI/ML Architect at 66degrees designs and ships production AI systems for enterprise clients and leads the engineers who build them. The Architect is the senior technical person on an engagement: the primary technical point of contact for the client, the owner of the architecture, and the person the team takes direction from.

The role is hands-on and client-facing in equal measure. Architects take a business problem, define the solution and the value it delivers, and then build and deploy it. They write and review code, make the architectural decisions, and stay with the system through production. They also run discovery, present to client executives, and are the face of 66degrees on the engagement.

This role is based in India and works daily with US-based colleagues and clients. Engagements typically run 12 to 16 weeks and are delivered by a Delivery Manager, one or more Architects, and a team of engineers.

• 2026 Google Cloud AI Partner of the Year. This team's work won the Artificial Intelligence category this year, one of four Partner of the Year awards 66degrees has earned. You're joining the top of the Google Cloud AI ecosystem

• About 60% of the work is generative and agentic AI, in production, for clients up to the Fortune 10. The rest is classical ML and MLOps, so you build real agents without losing the rest of the craft.

• New problem, new client, every 12 to 16 weeks. You build range fast and never spend three years on one system

• The whole stack. Architects span infrastructure, software, data, ML, AI, and analytics. The role is built to make you a well-rounded technologist, not to keep you in one lane

• Player-coach all the way up. Growth here doesn't mean giving up the craft. Leaders still build, and the management track is open because the team keeps growing, not because someone has to leave

• No up-or-out, no partner-track politics. Nobody is competing to differentiate themselves; everyone is trying to be the best technologist in the room, which is why people share what they know

• The team has grown roughly tenfold in three years and people stay. The few who've left went on to top tech firms and AI startups

• Design the architecture for each engagement: data pipelines, classical ML, agentic and generative AI, and the production path on Google Cloud, with the trade-offs written down

• Write and review the code that matters: core logic, orchestration, evaluation, the parts an engineer shouldn't be guessing at

• Take systems to production and own them through handoff: deployment, monitoring, drift, retraining, incident response, and a client who can run it after we leave

• Choose the right approach for the problem, and defend it on cost, explainability, and fit rather than on what's fashionable

• Direct a team of engineers: write the brief with the reasoning, not only the tasks; confirm it landed before code is written; review the work

• Run a team where engineers raise problems early and are heard when they do

• Develop engineers: pairing, giving them the client call, making them present the design review, and telling the Delivery Manager who is ready for more

• Delegate real work and still own the hard parts. Not a solo builder; not a delegator who only checks in

• Run discovery: turn an ambiguous ask into a scoped plan with success criteria the client agrees to

• Hold the solution to what the client actually needs, and when scope and need diverge, raise it early with options and what each costs

• Raise risk the day you see it, up and across, in business terms, with options. Report "on track" only when it is

• Present to client executives: the design, the trade-offs, the status, and the recommendation, answer first

• Spot expansion: where the client's next problem is, and tell sales

• Has personally designed and shipped production ML or AI systems, more than once, and owned them after launch. This is a hard requirement: an Architect who cannot point to systems they built and ran is not an Architect, whatever else is on the resume

• A data science foundation with range: several technique families (classification, forecasting, NLP, deep learning, recommendation, anomaly detection) each tied to a real business outcome. Depth in a few, breadth across the rest

• Has built a generative or agentic AI system that real users depended on: the architecture end to end, where the tools ran, how cost and latency were handled, and how quality was measured. A serious RAG system or single-agent workflow with real evaluation discipline qualifies; a chatbot demo does not

• Production fluency on a hyperscaler: Google Cloud preferred (Vertex AI, BigQuery, Cloud Run or GKE); deep AWS or Azure experience with a credible translation is acceptable

• Strong software engineering: expert Python and SQL, containers, CI/CD, and the habits that make code someone else can run

• Currently hands-on, with recent, specific examples of code written and architectural decisions made

• Currently leads a team of engineers or data scientists, delegating real work while owning the architecture and core logic

• A repeatable method for taking an engagement from discovery through handoff: phases, milestones, who does what, validation before anything ships

• A track record of reshaping scope when the original ask wasn't the right solution, and of making the business case for the change

• Answers the question asked, first, then the detail. Adjusts depth for the audience: can explain a system to an engineer and to a CFO in the same hour

• Executive presence: speaks with conviction, holds a position under challenge, and is someone we would put in front of a client executive next week without coaching

• Raises risk before it becomes a problem, to the person who can act on it, with a proposed fix. Comfortable disagreeing with senior people when the work calls for it

• Comfortable operating with autonomy across a significant time-zone gap with US colleagues and clients

• Bachelor's degree in computer science, data science, engineering or a related field, or equivalent experience

• Consulting or professional services background, with fluency in SoWs, scope, and billable delivery

• Google Cloud certifications, or delivery alongside Google teams

• Experience with agentic frameworks and patterns in production: tool calling, state management, MCP, multi-step orchestration, policy-guarded loops

• Regulated-industry delivery (financial services, healthcare) where explainability and audit are requirements, not features

• Daily use of AI coding assistants and agents in your own work, with a view on where they help and where they get in the way

• Experience working with a US-based team from India, and a point of view on how to make it work

We write this down so nobody is surprised in month two.

• The client sees you. You present the design, you deliver the bad news, you defend the recommendation. The strength of the 66degrees brand on an engagement is the Architect leading it

• The plan will be wrong sometimes. Client problems rarely arrive fully defined, and scope moves. Working out what's really needed and saying so is the job, not an exception to it

• Your engineers learn from you. If you raise risks, they will. If you don't, they won't. You set the standard for how the India team works

• The US is asleep for half your day. You will make decisions with incomplete direction and own them. This role works a shifted schedule: online through 2:00 PM US Eastern (11:30 PM IST), so that most of the US working day overlaps with yours

• It's hands-on. If your week has become meetings and status calls, this is not the role

66degrees is a leading consulting and professional services company specializing in developing AI-focused, data-led solutions leveraging the latest advancements in cloud technology. With our unmatched engineering capabilities and vast industry experience, we help the world's leading brands transform their business challenges into opportunities and shape the future of work.

At 66degrees, we believe in embracing the challenge and winning together. These values not only guide us in achieving our goals as a company but also for our people. We are dedicated to creating a significant impact for our employees by fostering a culture that sparks innovation and supports professional and personal growth along the way.

The AI/ML practice spans roughly 100 specialists across the U.S., Canada, the UK, France, and India, working across three pillars: agentic and generative AI; classical ML; and MLOps and AgentOps. The India team is being built to own engagements end to end, with a path from Engineer to Architect that runs through India.