AI/ML Engineer

We are hiring a Senior AI/ML Engineer who brings strong experience across Data Science, Data Engineering, and Generative AI/LLM development . This role will work on end-to-end AI solutions — from building datasets and pipelines, to developing LLM applications, to deploying models in production. Candidates may come from either a Data Scientist background, a Data Engineer background, or a hybrid AI/ML engineering background. If you have strong experience with LLMs, Generative AI systems, data pipelines, or applied machine learning, we want to speak with you. What You Will Do ---------------------------------------------------------------------------------------- LLM & Generative AI Development Build, fine-tune, and evaluate LLMs for chatbots, agents, summarization, classification, and automation workflows Design prompt engineering, prompt chaining, and RAG (retrieval-augmented generation) pipelines Implement embeddings, vector search, and hybrid search workflows Conduct model evaluation using quantitative and qualitative metrics Prototype and ship applied AI solutions that solve real business problems Machine Learning & Data Science Perform data exploration, feature engineering, hypothesis testing, and modeling Build predictive and classification models using modern ML techniques (transformers & classical ML) Create datasets for model training, fine-tuning, and evaluation Build dashboards, insights, and analytics to support product decisions Data Engineering & ML Infrastructure Build scalable ETL/ELT pipelines for ingestion, transformation, and model readiness Integrate data from APIs, databases, cloud services, and unstructured sources Prepare vectorization pipelines for RAG and LLM applications Support deployment of AI/ML models using APIs, containers, and microservices Develop monitoring, logging, CI/CD and automated workflows for stable production systems Cloud, Storage & Performance Work with Azure/AWS/GCP for storage, compute, and networking Manage SQL/NoSQL databases, warehouses, and data lakes Optimize pipelines, improve reliability, and ensure scalability Maintain performance dashboards and observability tools ⭐ Ideal Candidate Profile ---------------------------------------------------------------------------------------- You may fit one (or more) of these backgrounds: Core Technical Skills Strong Python programming skills Strong SQL skills (data modeling, queries, optimization) Experience with cloud platforms (Azure preferred , AWS/GCP also welcome) Experience with APIs, data ingestion, logs, and structured + unstructured data Experience supporting large-scale AI/ML workloads and production systems LLMs & Generative AI Hands-on experience with OpenAI , Llama , HuggingFace , Anthropic , or similar LLM providers Strong understanding of transformers, NLP fundamentals, embeddings, and tokenization Experience with RAG , vector search, and embeddings Familiarity with vector databases (Pinecone , FAISS , Chroma , etc.) Ability to design, evaluate, and optimize prompts and LLM workflows Experience building chatbots, agents, summarizers, classifiers, or GenAI automation tools Machine Learning & Statistical Foundations Solid grounding in statistics , probability , and machine learning concepts Experience building and validating ML models (traditional ML or deep learning) Ability to evaluate model performance using quantitative and qualitative metrics Experience preparing datasets for training, fine-tuning, and evaluation Data Engineering & Pipelines Experience building and maintaining ETL/ELT pipelines Strong data modeling, schema design, and data quality practices Experience integrating data from APIs, DBs, cloud systems, and external sources Experience with pipelines for embeddings, vectorization, and model preparation Familiarity with streaming/real-time systems (Kafka, EventHub) is a plus ML Ops & Infrastructure Familiarity with ML Ops tooling and workflows (CI/CD, testing, monitoring) Experience deploying models via REST APIs, Docker, containers, or microservices Ability to design stable, scalable, and reliable AI/ML infrastructure Experience with GPUs, compute optimization, or distributed systems is a plus End-to-End AI/ML Engineering Comfortable working across both model development and data/infrastructure Ability to design and deliver end-to-end solutions from ingestion → processing → model → deployment Strong problem solver who can translate business needs into technical architectures Required Skills ---------------------------------------------------------------------------------------- 3–7+ years of experience in Data Science, Data Engineering, ML Engineering, or AI Engineering Strong Python skills Experience with LLMs, Generative AI, or ML modeling Understanding of cloud environments (Azure/AWS/GCP) Experience with SQL, APIs, and data modeling Ability to turn business problems into technical architectures ✨ Nice-to-Have ---------------------------------------------------------------------------------------- Experience fine-tuning LLMs (LoRA, QLoRA) Experience deploying models on GPUs Experience with Kubernetes, Docker, Terraform/Bicep Familiarity with streaming systems (Kafka, EventHub) Experience with multi-agent workflows How to Apply ---------------------------------------------------------------------------------------- If you're passionate about building real AI systems — not just prototypes — and want to work on impactful, production-grade solutions, we’d love to hear from you. Please share your resume at or apply here with your resume.