Immediate joiners Ai Ml Engineer

About the Role

We are seeking an exceptional AI/ML Engineer who goes beyond traditional boundaries. This is not a role for someone who only trains models or works in Jupyter notebooks. We need a full-stack product engineer with deep AI/ML expertisesomeone who can architect scalable systems, ship production-grade code, own product outcomes, and drive technical decisions from conception to deployment.

You will 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 thrive on seeing your work in users hands and measure success by product impact rather than model accuracy alone, this role is for you.

Key Responsibilities

Product Development & Delivery

Own AI/ML products end-to-end—from ideation to production.

Define technical architecture, make build-vs-buy decisions, scope MVPs, and deliver impactful features.

Collaborate with product managers and designers while driving technical strategy independently.

End-to-End ML Systems

Design and implement complete ML pipelines: data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.

Build maintainable, scalable, and production-ready systems—not just experimental notebooks.

Production Engineering

Write clean, tested, production-grade code across the stack.

Build RESTful APIs, implement efficient data pipelines, optimize model serving infrastructure, and ensure reliability and performance at scale.

Technical Architecture

Make critical architectural decisions around model selection, infrastructure design, data flow, and system integration.

Evaluate trade-offs, prototype solutions, and champion best practices.

Cross-Functional Leadership

Collaborate with engineering, product, design, and business teams to translate requirements into technical solutions.

Communicate complex technical concepts clearly and drive alignment on priorities.

Performance & Optimization

Continuously improve system performance, model accuracy, latency, and resource utilization.

Implement monitoring and observability for production models and iterate based on 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.

Proven track record of shipping real products—not just research projects or POCs.

ML Engineering Excellence

Strong fundamentals in ML with hands-on experience in NLP, computer vision, recommendation systems, or time-series forecasting.

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

Software Engineering Skills

Strong programming skills in Python and 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

Experience with model serving (TensorFlow Serving, TorchServe, ONNX), feature stores, experiment tracking (MLflow, Weights & Biases), and CI/CD for ML.

Familiarity with containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure).

Full-Stack Capabilities

Ability to build complete features end-to-end.

Experience with backend development (FastAPI, Flask), API design, databases (SQL/NoSQL), caching strategies, and basic frontend skills.

Data Engineering Skills

Strong SQL and data manipulation skills.

Experience building ETL/ELT pipelines and working with large-scale structured and unstructured data.

Product Mindset

Ability to translate ambiguous requirements into pragmatic technical solutions.

Focus on user impact and business outcomes.

System Design

Expertise in designing robust, scalable systems considering performance, reliability, security, and cost.

Experience with distributed systems and microservices architecture.

Preferred Skills

Exposure to LLM frameworks (LangChain, LlamaIndex, Haystack), vector databases (Pinecone, Weaviate, Qdrant), and RAG architectures.

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

Understanding of MLOps best practices for monitoring, versioning, and governance.

Experience with A/B testing and experimentation frameworks.

What Sets You Apart

Built AI features used by thousands or millions of users.

Strong opinions on engineering practices but pragmatic about trade-offs.

Mentored engineers and elevated team standards.

Comfortable with ambiguity and minimal guidance.

Experience handling model drift, data quality issues, and production incidents.