Lead Machine Learning Engineer

Location - Chennai / Mumbai / Pune / Hyderabad / Bangalore (Hybrid)

About Us

Zemoso Technologies is a Software Product Market Fit Studio that brings silicon valley style

rapid prototyping and rapid application builds to Entrepreneurs and Corporate innovation. We

offer Innovation as a service and work on ideas from scratch and take it to the Product Market

Fit stage using Design Thinking -> Lean Execution -> Agile Methodology.

We were featured as one of Deloitte Fastest 50 growing tech companies from India thrice (2016,

2018 and 2019). We were also featured in Deloitte Technology Fast 500 Asia Pacific both in

2016 and 2018.

We are located in Hyderabad, India, and Dallas, US. We have recently incorporated another

office in Waterloo, Canada. Our founders have had past successes - founded a decision

management company acquired by SAP AG (now part of Hana Big data stack & NetWeaver

BPM), early engineering team of Zoho (leading billion $ SaaS player) & some Private Equity

experience. Marquee customers along with some exciting start-ups are part of our clientele.

Role Summary

We are seeking a highly experienced Machine Learning Lead to drive the architecture,

development, and deployment of advanced machine learning solutions. In this role, you will not

only lead a talented technical team but also serve as the critical bridge between our engineering

efforts and our clients. You must possess the unique ability to distill complex, highly technical

ML concepts into clear, business-driven language for stakeholders, ensuring the successful

delivery of complex projects.

What You Will Do

Stakeholder Communication & Client Management: Act as the primary technical

liaison for clients. Translate complex ML terms, model behaviors, and architectural

trade-offs into actionable business insights for non-technical stakeholders.

Technical Leadership: Architect and design end-to-end ML solutions. Lead, mentor,

and guide a team of Data Analysts and ML/Data Engineers through the entire project

lifecycle.

Project Delivery: Oversee the collection, cleanup, exploration, and statistical analysis of

complex datasets to drive business intelligence.

Model Lifecycle Management: Lead the implementation, deployment, and scaling of

advanced ML models and algorithms to solve complex business problems.

Cross-functional Collaboration: Work closely with data engineers to design, build,

test, and monitor robust data and MLOps pipelines for ongoing business operations.

Strategic Alignment: Understand the client's core business model to ensure the ML

solutions built bring measurable, actionable ROI out of data available in various formats.

Basic Qualifications

Experience: 8 to 12 years of overall industry experience, with a proven track record in

Data Science, Machine Learning, and technical leadership.

Client-Facing Expertise: Demonstrated experience in stakeholder management,

specifically the ability to confidently answer to clients and demystify complex ML

concepts in a consultative manner.

Technical Proficiency: Exceptional, hands-on coding experience in Python and robust

experience with popular ML frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch).

Analytical Rigor: Deep expertise in statistical modeling of large data sets and a

comprehensive understanding of diverse ML algorithms.

Pipeline & Architecture: Strong experience designing robust data/ML pipelines and

transitioning models from experimentation to production environments.

Data Analytics: Solid foundational experience in data analytics, including the ability to

extract actionable insights from raw data (experience with advanced Excel/BI tools is a

plus).

Nice to Have Qualifications

Hands-on experience with Deep Learning, Generative AI, or NLP frameworks.

Experience with MLOps practices and tools (e.g., MLflow, Kubeflow, Docker, Kubernetes).

Experience with Cloud platforms (AWS, GCP, or Azure) and their respective ML services.

A background working in fast-paced startup environments or consulting/services agencies.

Benefits

Competitive salary.

Hybrid work model.

Learning and gaining experience rapidly.

Reimbursement for basic working set up at home.