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 DevelopmentBuild, fine-tune, and evaluate LLMs for chatbots, agents, summarization, classification, and automation workflowsDesign prompt engineering, prompt chaining, and RAG (retrieval-augmented generation) pipelinesImplement embeddings, vector search, and hybrid search workflowsConduct model evaluation using quantitative and qualitative metricsPrototype and ship applied AI solutions that solve real business problemsMachine Learning & Data SciencePerform data exploration, feature engineering, hypothesis testing, and modelingBuild predictive and classification models using modern ML techniques (transformers & classical ML)Create datasets for model training, fine-tuning, and evaluationBuild dashboards, insights, and analytics to support product decisionsData Engineering & ML InfrastructureBuild scalable ETL/ELT pipelines for ingestion, transformation, and model readinessIntegrate data from APIs, databases, cloud services, and unstructured sourcesPrepare vectorization pipelines for RAG and LLM applicationsSupport deployment of AI/ML models using APIs, containers, and microservicesDevelop monitoring, logging, CI/CD and automated workflows for stable production systemsCloud, Storage & PerformanceWork with Azure/AWS/GCP for storage, compute, and networkingManage SQL/NoSQL databases, warehouses, and data lakesOptimize pipelines, improve reliability, and ensure scalabilityMaintain performance dashboards and observability tools⭐ Ideal Candidate Profile----------------------------------------------------------------------------------------You may fit one (or more) of these backgrounds:Core Technical SkillsStrong Python programming skillsStrong 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 dataExperience supporting large-scale AI/ML workloads and production systemsLLMs & Generative AIHands-on experience with OpenAI , Llama , HuggingFace , Anthropic , or similar LLM providersStrong understanding of transformers, NLP fundamentals, embeddings, and tokenizationExperience with RAG , vector search, and embeddingsFamiliarity with vector databases ( Pinecone , FAISS , Chroma , etc.)Ability to design, evaluate, and optimize prompts and LLM workflowsExperience building chatbots, agents, summarizers, classifiers, or GenAI automation toolsMachine Learning & Statistical FoundationsSolid grounding in statistics , probability , and machine learning conceptsExperience building and validating ML models (traditional ML or deep learning)Ability to evaluate model performance using quantitative and qualitative metricsExperience preparing datasets for training, fine-tuning, and evaluationData Engineering & PipelinesExperience building and maintaining ETL/ELT pipelinesStrong data modeling, schema design, and data quality practicesExperience integrating data from APIs, DBs, cloud systems, and external sourcesExperience with pipelines for embeddings, vectorization, and model preparationFamiliarity with streaming/real-time systems (Kafka, EventHub) is a plusML Ops & InfrastructureFamiliarity with ML Ops tooling and workflows (CI/CD, testing, monitoring)Experience deploying models via REST APIs, Docker, containers, or microservicesAbility to design stable, scalable, and reliable AI/ML infrastructureExperience with GPUs, compute optimization, or distributed systems is a plusEnd-to-End AI/ML EngineeringComfortable working across both model development and data/infrastructureAbility to design and deliver end-to-end solutions from ingestion → processing → model → deploymentStrong problem solver who can translate business needs into technical architecturesRequired Skills----------------------------------------------------------------------------------------3–7+ years of experience in Data Science, Data Engineering, ML Engineering, or AI EngineeringStrong Python skillsExperience with LLMs, Generative AI, or ML modelingUnderstanding of cloud environments (Azure/AWS/GCP)Experience with SQL, APIs, and data modelingAbility to turn business problems into technical architectures✨ Nice-to-Have----------------------------------------------------------------------------------------Experience fine-tuning LLMs (LoRA, QLoRA)Experience deploying models on GPUsExperience with Kubernetes, Docker, Terraform/BicepFamiliarity with streaming systems (Kafka, EventHub)Experience with multi-agent workflowsHow 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.