Senior / Principal AI & Machine Learning Engineer

Overview

Role

Location

Salary

Overview

What You’ll Be Doing

Core Responsibilities Include

• Designing and building end-to-end machine learning systems in production environments
• Developing and deploying scalable AI/ML models (supervised, unsupervised, deep learning, and generative AI use cases)
• Leading architecture decisions for ML pipelines, feature stores, training infrastructure, and inference systems
• Building and optimising LLM-based applications (RAG systems, fine-tuning, prompt engineering, evaluation pipelines)
• Owning MLOps practices: CI/CD for ML, model versioning, monitoring, drift detection, and retraining pipelines
• Collaborating closely with product, engineering, and data teams to translate business problems into scalable AI solutions
• Improving model performance, latency, cost efficiency, and reliability in production
• Mentoring engineers and setting technical standards across AI/ML teams
• Contributing to platform-level decisions around data, AI infrastructure, and cloud architecture

Candidate Profile

• 7–15+ years' experience in software engineering, data science, or ML engineering
• Strong background in product companies, scale-ups, or enterprise AI platforms
• Previously built production-grade AI systems, not just notebooks or POCs
• Comfortable owning systems end-to-end (data → model → deployment → monitoring)
• Often ex-FAANG, major tech firms, AI startups, or high-performing regional tech companies
• Strong engineering mindset (not just modelling)
• Product-first engineering, not research-only profiles
• Strong Python engineering with systems thinking
• Used to fast iteration and deploying models into live environments
• Comfortable working directly with stakeholders and product owners

Machine Learning And Ai

• PyTorch
• TensorFlow

• XGBoost And LightGBM

• Hugging Face ecosystem (Transformers, Datasets, Diffusers)
• OpenAI / Anthropic APIs (production LLM integration)
• LangChain / LlamaIndex (agentic workflows, RAG systems)
• LLM / GenAI Stack (very important in 2026)
• RAG architectures (vector DB + retrieval pipelines)
• Embedding models (OpenAI, Cohere, open-source models)
• Vector databases: Pinecone, Weaviate, Milvus, FAISS
• Fine-tuning frameworks (LoRA, PEFT)
• Evaluation frameworks (RAGAS, custom eval pipelines)

Mlops And Production

• Docker, Kubernetes

• Airflow And Dagster And Prefect

• CI/CD tools (GitHub Actions, GitLab CI)
• Model monitoring (Evidently AI, Arize, custom observability stacks)

Cloud Platforms

• AWS (SageMaker, EKS, S3, Lambda) or Azure ML / Azure Databricks

Data Stack

• Databricks
• Spark (PySpark essential)
• Delta Lake / Iceberg / Lakehouse architectures