Design, build, and maintain AI/GenAI-powered applications , leveraging Python and modern frameworks. Work extensively with large language models (LLMs) implementing, fine-tuning, and managing them in production environments. Develop scalable APIs using FastAPI , ensuring high performance and low latency for AI-driven workflows. Perform data manipulation and analysis using libraries like Pandas and NumPy to support model development and experimentation. Apply software engineering best practices including version control ( Git ), automated testing, CI/CD pipelines, and code reviews. Collaborate with cross-functional teams to design and deploy end-to-end AI/ML solutions , integrating MLOps practices for reliability and scalability. Deploy, monitor, and optimize applications using Docker, Kubernetes, Jenkins, and Terraform , with a focus on advanced CI/CD practices . Explore and implement Agentic AI architectures and orchestration frameworks to support multi-agent workflows and autonomous decision-making. Role & responsibilities Key Qualifications & Experience Mandatory Skills: Strong proficiency in Python , with hands-on experience in AI/GenAI frameworks . Proven expertise in implementing and managing LLMs in production. Experience with FastAPI for API design and development. Solid foundation in software engineering principles , including Git, CI/CD, and automated testing. Background in MLOps, ML engineering, or Data Science with a proven track record of maintaining AI solutions. Proficiency in DevOps tools : Docker, Kubernetes, Jenkins, Terraform. Hands-on experience with Agentic AI implementation . Nice to Have: Familiarity with cloud platforms (Azure) for deploying GenAI solutions. Experience with vector databases (FAISS, Pinecone, Chroma, Weaviate). Knowledge of prompt engineering, chaining, and orchestration frameworks (LangChain, LangGraph, CrewAI). Understanding of GraphQL APIs and real-time system design. Soft Skills Strong analytical and problem-solving mindset. Ability to work in a collaborative and agile environment . Curiosity to explore emerging AI/Agentic frameworks . Excellent communication and documentation skills. Proactive ownership and initiative in delivering high-quality solutions.
Machine Learning Engineer : Key Responsibilities: Design and implement machine learning workflows from data ingestion to model deployment. Develop, train, and fine-tune supervised and unsupervised ML models for business applications. Translate complex business problems into ML-based solutions and measurable outcomes. Build and automate model training, evaluation, and deployment pipelines. Collaborate with cross-functional teams to understand requirements and deliver production-grade models. Ensure scalability, reliability, and performance of deployed models. Monitor model accuracy, drift, and performance, and manage continuous improvement cycles. Document ML experiments, workflows, and results for reproducibility. Required Skills & Qualifications: Bachelors or Masters degree in Computer Science, Artificial Intelligence, or related field. Strong hands-on experience in building and deploying ML models end-to-end. Expertise in machine learning algorithms, model evaluation, and workflow orchestration. Experience with ML frameworks such as Scikit-learn, TensorFlow, or PyTorch. Good understanding of MLOps practices, including CI/CD pipelines and model versioning. Experience in deploying ML solutions using APIs, containers, or cloud platforms (AWS, Azure, or GCP). Proven experience delivering real-world ML use cases such as classification, regression, forecasting, or recommendation systems. Good to Have: Exposure to automation frameworks or ML pipeline tools (MLflow, Airflow, Kubeflow, etc.). Familiarity with data versioning and monitoring tools. Understanding of data governance and model lifecycle management. Soft Skills: Strong problem-solving and analytical skills. Excellent communication and documentation abilities. Ability to work independently and within collaborative teams.