Artificial Intelligence Engineer

Why Wiom exists
90% of Indian households don’t have unlimited broadband internet! Enter Wiom.
Wiom is rebuilding home internet around how middle India actually buys. We offer reliable, unlimited
home internet in small, flexible recharges — including a ₹23 daily plan — making high-speed connectivity
accessible without large upfront commitments.
Internet is the first product. The bigger opportunity is the relationship we build with each household. The
same Wiom rail can carry entertainment, learning, AI and other digital services — Wiom TV is already live,
with learning and AI offerings in pilot. Our ambition: 50 crore people, ₹23 at a time — sachetised access
to the digital world.
Wiom was founded by IIT-IIM alumni with experience at companies including Microsoft and American
Express, and is backed by investors including Accel, Prosus, Bertelsmann, RTP Global, YourNest and
Auxano.
The Role
We’re looking for an AI Engineer II to design, build, and ship production-grade AI systems. You’ll work
across the full lifecycle — from experimenting with models and building retrieval pipelines to deploying
and monitoring systems that serve real users at scale. This role sits at the intersection of applied machine
learning and software engineering, so you’ll need to be equally comfortable fine-tuning a model and
writing the service that wraps it.
You’ll partner closely with product, data, and platform teams to turn ambiguous problems into reliable,
measurable AI features.
Workstreams You’ll Own (Like a Boss!)
• Build and productionize AI/ML systems, including large language model (LLM) applications, retrieval-
augmented generation (RAG) pipelines, and model-serving infrastructure.
• Design and implement RAG architectures: chunking and embedding strategies, vector store selection
and tuning, retrieval and re-ranking, and grounding responses to reduce hallucination.
• Fine-tune, adapt, and evaluate models using PyTorch, TensorFlow, and the Hugging Face ecosystem
(Transformers, Datasets, Tokenizers, PEFT/LoRA).
• Develop robust evaluation frameworks — offline benchmarks, human-in-the-loop review, and online
metrics — to measure quality, latency, and cost.
• Optimize inference for performance and cost (quantization, batching, caching, GPU utilization).
• Write clean, well-tested, maintainable code and contribute to shared libraries and services.
• Monitor deployed systems for drift, regressions, and reliability, and iterate based on real-world
feedback.
• Collaborate with product and design to scope AI features, and communicate trade-offs clearly to
technical and non-technical stakeholders.
What makes you a great fit?
• Education: Bachelor’s or Master’s degree in Computer Science, Machine Learning, a related field, or
equivalent with 8-10 years of practical experience
• Python and engineering: Strong programming skills in Python, with solid software engineering
fundamentals (version control, testing, code review).
• AI Frameworks: Working knowledge of PyTorch, TensorFlow, and the Hugging Face ecosystem.
• RAG: Hands-on experience building retrieval-augmented generation systems — embeddings, vector
databases (e.g., FAISS, Pinecone, Weaviate, pgvector), and retrieval pipeline design.
• LLMs: Practical experience working with large language models, including prompting, fine-tuning, and
evaluation.
• Production: Experience deploying models or ML services to production (APIs, containers, cloud
environments).
• ML lifecycle: Familiarity with data preparation, experimentation, evaluation, deployment, and
monitoring.
• Research to code: Ability to read research papers and translate ideas into working implementations.
Good to have
• Orchestration: Experience with frameworks such as LangChain, LlamaIndex, or equivalent.
• Agents: Familiarity with agentic systems, tool use, and function calling.
• Inference: Experience with inference optimization (vLLM, TensorRT, ONNX, quantization) and GPU
workloads.
• MLOps: Exposure to MLflow, Weights & Biases, Kubeflow, and CI/CD for ML.
• Cloud: Experience with AWS, GCP, or Azure, and infrastructure-as-code.
• Responsible AI: Evaluation for safety, bias, and reliability.
• Proof of work: Open-source contributions or a portfolio of applied AI projects.
What it’s like to work at Wiom
We are going to be upfront. The way we work does NOT suit everyone. No old-fashioned hierarchy. No
micro-management. No hiding behind fancy job titles. We work through layers of self-sufficient,
autonomous teams — start-ups within start-ups within start-ups. Wiom is the place in your career where
you transition from management to ownership, eased by guidance and plenty of support from talented,
super-smart colleagues.
What we offer
• Competitive salary.
• Health and other benefits.
• Budget for compute, learning, and conferences.
• A collaborative team building AI products that reach real users.
Wiom is an equal opportunity employer. We welcome applicants of all backgrounds and are committed to building an inclusive team

Work arrangement
No