Machine Learning Engineer
Faculty
AWSAzureCloudDockerGoogle Cloud PlatformKubernetesPythonPyTorchScikit-LearnTensorflow
Job Level
SeniorLead
Tech Stack
AWSAzureCloudDockerGoogle Cloud PlatformKubernetesMicroservicesOpen SourcePythonPyTorchScikit-LearnTensorflow
About the role
- Define end-to-end architecture for AI/ML and Gen AI systems including data pipelines, model training/inference, and MLOps.
- Serve as a strategic technical advisor to clients, leading solution design discussions, presenting AI/ML architectures, and representing 3Pillar in client-facing interactions to drive innovation and business value.
- Architect scalable solutions using cloud-native AI tools (Azure ML, AWS SageMaker, or GCP Vertex AI) and integrate Generative AI into components/features leveraging LLMs via APIs (OpenAI, Gemini, etc.) or open source models like LLama.
- Design retrieval-augmented generation (RAG) systems with vector databases (Pinecone, Weaviate, FAISS and similar).
- Guide teams on MLOps frameworks for CI/CD, model versioning, monitoring, and automated retraining.
- Evaluate build-vs-buy decisions and benchmark AI tools/platforms.
- Evaluate emerging technologies and trends in AI, ML, Gen AI space and recommend adoption strategies.
- Mentor technical teams and guide solution architects, data engineers, and ML engineers.
- Ensure ethical and responsible AI practices including bias detection, interpretability, and governance.
- Act as a subject matter expert, providing guidance and mentorship to junior colleagues and strengthen the expertise in the organization.
- Collaborate with leadership to align machine learning strategies with overall business objectives.
- Represent the organization at industry conferences, workshops, and events.
- Drive cross-functional collaboration to identify opportunities for applying machine learning to business challenges.
Requirements
- Master’s degree in Computer Science, Engineering, Mathematics, or a related field with 12+ years of industry experience in machine learning or data science (Ph.D. highly desirable).
- Strong grasp of AI architecture patterns (RAG, agent-based systems, prompt orchestration).
- Deep experience with Python and ML libraries (scikit-learn, XGBoost, PyTorch, TensorFlow).
- Hands-on with Gen AI APIs (OpenAI, Claude, Gemini), prompt engineering, embeddings, and fine-tuning.
- Experience designing enterprise AI systems with MLOps (MLflow, Kubeflow, SageMaker Pipelines).
- Familiarity with APIs, microservices, and containerization (Docker, Kubernetes).
- Experience in Data Governance, Model Risk Management, and compliance.
- Extensive knowledge of machine learning theory, algorithms, and methodologies.
- Strong leadership and communication skills, with ability to influence stakeholders at all levels.
- Demonstrated ability to think strategically and drive innovation.
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