AI/ML Engineer

Title: AI/ML Engineer

Experience: 5+ Years

Location: -Pune preferred/Hyderabad (4 Days WFO)

About the Role We're seeking an exceptional AI/ML Engineer who breaks the traditional

mold. This isn't a role for someone who only trains models or lives in Jupyter notebooks.

We need an end-to end product engineer who happens to have deep AI/ML expertise—

someone who can architect scalable systems, ship production code, own product

outcomes, and drive technical decisions from conception to deployment.

You'll be responsible for building and scaling AI-powered products that directly impact our

users and business.

This means taking models from research to production, designing robust APIs, optimizing

infrastructure, collaborating with cross-functional teams, and owning the complete

product lifecycle.

If you're a builder who thrives on seeing your work in users' hands and measures success

by product impact rather than model accuracy alone, this role is for you.

What You'll Own Product Development & Delivery: You'll own entire AI/ML products from

ideation to production.

This includes defining technical architecture, making build-vs-buy decisions, scoping

MVPs, and delivering features that solve real user problems.

You'll work closely with product managers and designers, but you'll drive technical strategy

and execution independently. End-to-End ML Systems: Design and implement complete

ML pipelines including data ingestion, feature engineering, model training, evaluation,

deployment, and monitoring. You'll build systems that are maintainable, scalable, and

production-ready—not just experimental notebooks.

Production Engineering: Write clean, tested, production-grade code across the stack. Build

RESTful APIs, implement efficient data processing pipelines, optimize model serving

infrastructure, and ensure systems are reliable, performant, and cost-effective at scale.

Technical Architecture: Make critical architectural decisions around model selection,

infrastructure design, data flow, and system integration. You'll evaluate trade-offs between

different approaches, prototype solutions, and champion best practices across the team.

Cross-Functional Leadership: Collaborate with engineering, product, design, and business

teams to translate requirements into technical solutions. You'll advocate for users,

communicate complex technical concepts clearly, and drive alignment on priorities and

timelines.

Performance & Optimization: Continuously improve system performance, model accuracy,

latency, and resource utilization. Implement monitoring and observability to catch issues

early, and iterate based on production metrics and user feedback. What We're Looking For

Experience Profile: 4-6 years of software engineering experience with at least 3 years

building and deploying AI/ML systems in production environments. You've shipped real

products that users depend on, not just research projects or POCs. ML Engineering

Excellence: Strong fundamentals in machine learning with hands-on experience across

multiple domains—NLP, computer vision, recommendation systems, or time-series

forecasting. You understand model selection, training strategies, evaluation metrics, and

when to use different architectures.

Proficiency with PyTorch or TensorFlow, scikit-learn, and modern ML frameworks. Software

Engineering Chops: You're a strong programmer who writes clean, maintainable code.

Solid proficiency in Python with experience in at least one additional language (Go, Java,

JavaScript, or C++).

Deep understanding of data structures, algorithms, design patterns, and software

architecture principles. Production ML Systems: Proven track record building scalable ML

infrastructure including model serving (TensorFlow Serving, TorchServe, ONNX), feature

stores, experiment tracking (MLflow, Weights & Biases), and CI/CD for ML.

Experience with containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP, or

Azure). Full-Stack Capabilities: Ability to build complete features end-to-end. Experience

with backend development (FastAPI, Flask), API design, databases (SQL and NoSQL),

caching strategies, and basic frontend skills when needed.

You're comfortable working across the stack. Data Engineering Skills: Strong SQL and data

manipulation skills with experience building ETL/ELT pipelines. Proficiency with data

processing frameworks (Spark, Dask, or similar) and working with both structured and

unstructured data at scale.

Product Mindset: You think beyond technical implementation to user impact and business

outcomes. Experience working closely with product teams, translating ambiguous

requirements into technical solutions, and making pragmatic engineering decisions that

balance quality, speed, and scope.

System Design: Ability to design robust, scalable systems considering performance,

reliability, security, and cost. Experience with distributed systems, microservices

architecture, and handling high-traffic production environments. Technical Stack Exposure

Experience with modern LLM frameworks (LangChain, LlamaIndex, Haystack), vector

databases (Pinecone, Weaviate, Qdrant), and RAG architectures is highly valued.

Familiarity with model optimization techniques (quantization, pruning, distillation) and

serving optimizations. Understanding of MLOps best practices and tools for model

monitoring, versioning, and governance. What Sets You Apart You've built AI features that

thousands or millions of users interact with daily.