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Design architecture for each engagement, including data pipelines, ML, and generative/agentic AI on Google Cloud
Write and review core code, orchestration, and evaluation logic
Take systems to production and own them through deployment, monitoring and retraining
Direct and review a team of engineers, including briefs and design reviews
Run discovery and present designs, trade-offs and status to client executives
Design the architecture for each engagement: data pipelines, classical ML, agentic and generative AI, and the production path on Google Cloud
Write and review the code that matters: core logic, orchestration, evaluation
Take systems to production and own them through handoff: deployment, monitoring, drift, retraining, incident response
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 discovery: turn an ambiguous ask into a scoped plan with success criteria the client agrees to
Leadership and delivery (required)
Bachelor's degree in computer science, data science, engineering or a related field, or equivalent experience
Experience with agentic frameworks and patterns in production: tool calling, state management, MCP, multi-step orchestration, policy-guarded loops
Communication and judgment (required)
Experience working with a US-based team from India, and a point of view on how to make it work
Education - Bachelor's degree in computer science, data science, engineering or a related field, or equivalent experience Preferred
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- 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
- 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
Why this role - 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.
Build - 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
- 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
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New Data Leadership postings at the employers we follow are up 7% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Leadership, by the work the posting describes.
Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.
Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.
Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.
The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.
66degrees is an end-to-end AI transformation partner that guides enterprises from complex business challenges to clear, quantifiable outcomes. Our company is the culmination of several successful firms, each a leader in its own right in cloud, artificial intelligence, and data. This convergence of talent and expertise is how we help businesses reach their own "inflection point," where chaotic data becomes a strategic asset, complexity becomes clarity, and AI becomes an engine for growth. Our ultimate vision is to be the catalyst for a future where every business operates as an intelligent enterprise, with autonomous systems unlocking human potential.
At 66degrees, we believe in thriving through challenges 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.
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.
Please note, we cannot sponsor or transfer any visas, of any kind, at this time.
66degrees is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to actual or perceived race, color, religion, sex, gender, gender identity, national origin, age, weight, height, marital status, sexual orientation, veteran status, disability status or other legally protected class.
As an AI transformation partner, 66degrees leverages intelligent solutions to enhance our recruitment experience. We utilize AI tools—including LinkedIn Recruiter’s Hiring Assistant and interview transcription technologies—to assist with sourcing, role analysis, and capturing interview highlights.
These tools augment our process, but we "Commit to Our Craft" by ensuring all final hiring decisions are made by our human Talent Team. By applying, you acknowledge the use of these technologies to help us "Win Together" in finding the best fit for our team.
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