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Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Leads the architecture, development, and production delivery of AI/ML capabilities in enterprise healthcare applications. Builds ML and generative AI solutions, RAG systems, model-serving APIs, MLOps workflows, distributed cloud services, and CI/CD pipelines. Ensures responsible AI, security, privacy, reliability, monitoring, and operational readiness. Influences architecture across teams, mentors engineers, documents technical decisions, and delivers reusable production components.
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APIsCi/CdCloud ComputingContainersDistributed SystemsGenerative AiInfrastructure As CodeLarge Language Models (Llms)Machine Learning FrameworksMachine Learning Operations (Mlops)Model RegistriesPythonRetrieval-Augmented Generation (Rag)Vector Search
Consumer Web • HR Tech
Design, build, and operate production-grade autonomous AI systems and multi-agent workflows. Develop agentic capabilities using OpenAI, Google Gemini, and Perplexity; implement orchestration, reflection, planning, evaluation, observability, and human-in-the-loop controls. Deploy scalable serverless services with Python and AWS, integrate with .NET systems, manage reliability and cost constraints, and create testing frameworks to monitor and improve agent quality.
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Artificial Intelligence • Information Technology • Consulting • Automation
Build and manage Azure Databricks jobs, lakehouse pipelines, reusable Python/PySpark scripts, and cloud-native microservices. Develop LLM-powered copilots, agentic workflows, RAG pipelines, prompt strategies, guardrails, and AI inference services. Integrate enterprise data with AI solutions, optimize performance and costs, implement governance and observability, and contribute to architecture and technical design. The role also involves independent ownership, code reviews, mentoring, stakeholder collaboration, and delivering production-grade AI solutions.
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Azure Data Lake Storage Gen2Azure DatabricksAzure DevopsAzure Event HubsAzure OpenaiBicepDelta LakeDockerEmbeddingsFastapiFlaskGitGithub ActionsJavaKafkaLangchainOpenaiPysparkPythonRagRest ApisSemantic KernelSpark SqlTerraformVector Databases
Assist in designing, training, optimizing, and deploying machine learning and deep learning solutions. Responsibilities include data preprocessing, REST API and service integration, model evaluation, fine-tuning, scalability improvements, and collaboration with data scientists, software engineers, and product managers. The role also involves generative AI work with LLMs, prompt engineering, and retrieval-augmented generation.
We are looking for a motivated AI Engineer with 1–2 years of experience to join our development team. You will assist in designing, building, and deploying AI solutions that solve real-world business problems. Your role will bridge the gap between experimental research and production-ready applications, focusing on data preprocessing, model training, and performance optimization.
Key Responsibilities
- Model Development & Training: Assist in the design and implementation of machine learning and deep learning models using frameworks like TensorFlow or PyTorch.
- Data Preprocessing: Clean, normalize, and augment large datasets to ensure high-quality inputs for model training.
- API & Service Integration: Develop and maintain REST APIs (using FastAPI or Flask) to serve AI models to end-user applications.
- Testing & Optimization: Conduct model evaluation and fine-tuning to improve accuracy, latency, and scalability.
- Collaboration: Work closely with data scientists, software engineers, and product managers to align AI features with business goals.
- Generative AI (Modern Requirement): Many current roles for this experience level now require hands-on experience with LLMs, prompt engineering, and RAG (Retrieval-Augmented Generation).
Requirements
Required Skills and Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related field.
- Programming: High proficiency in Python (including libraries like NumPy, Pandas, and Scikit-learn).
- Machine Learning Fundamentals: Solid understanding of supervised/unsupervised learning, neural networks, and evaluation metrics (e.g., F1 score, RMSE).
- Software Engineering: Experience with Git for version control and basic knowledge of Docker for containerization.
- Mathematical Foundation: Strong grasp of linear algebra, calculus, and statistics.
- Cloud Basics: Exposure to cloud AI services such as AWS SageMaker, Google Vertex AI, or Azure Machine Learning.
Preferred/Nice-to-Have Skills
- MLOps: Familiarity with MLflow or Weights & Biases for experiment tracking.
- Database Knowledge: Experience with SQL and NoSQL databases like PostgreSQL or MongoDB.
- GenAI Tools: Experience with LangChain or LlamaIndex for building LLM applications.
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