Purpose & Overall Relevance for the Organization

A Machine Learning Engineer applies foundational knowledge of the end-to-end Model Development Lifecycle (MDLC), software engineering, cloud technologies, and modern AI methodologies to help build, deploy, and scale machine learning solutions. They collaborate with cross-functional teams to transform proofs-of-concept into reliable and scalable production systems — with growing focus on Generative AI and agentic AI frameworks.

Key Responsibilities

Machine Learning Engineering

• Support the design and development of ML components for data and ML infrastructure (data pipelines, feature stores, model training/inference services)
• Assist in implementing end-to-end ML pipelines (MLOps), including data ingestion, feature engineering, training, deployment, and model monitoring
• Work with data scientists to productionize models and ensure business value is consistently delivered
• Contribute to model observability — logging, drift tracking, performance dashboards

GenAI & Agentic AI

• Use LLMs, prompt engineering, embeddings, and vector stores to enable intelligent applications
• Build small-scale AI agents using frameworks like LangChain, LlamaIndex, or equivalent
• Experiment with responsible and explainable use of foundation models to solve clear business problems

Analytics

• Assist in applying machine learning techniques with guidance from senior engineers or data scientists.Perform exploratory data analysis and support feature selection and data preparation
• Use unsupervised learning when appropriate for early insights or pattern discovery

Data Management & Engineering

• Support creation, improvement, and validation of curated datasets for ML applications
• Contribute to data quality checks, schema design, and efficient feature retrieval
• Follow best practices for security, accessibility, and ethical use of data.

Programming / Software Development

• Write clean, reliable, well-tested code (primarily in Python)
• Implement and maintain CI/CD workflows for ML components with supervision
• Deploy ML workloads on cloud or on-prem environments using modern tooling.

Visualization & Storytelling

• Build automated dashboards to support model/data health visibility
• Communicate insights clearly to technical and non-technical stakeholders.

Testing & Reliability

• Contribute to writing unit, integration, and regression tests for ML components
• Monitor test outcomes and support issue resolution.

Education & Experience — Minimum Qualifications

• 1+ years experience in a Machine Learning, Data Engineering, or AI-focused software engineering role (internships and academic projects count)
• Bachelor's degree in Computer Science, Engineering, Mathematics, or related field (Master’s not required)
• Solid understanding of Python, data structures, and basic software engineering practices
• Familiarity with:
  • ML frameworks: scikit-learn, TensorFlow, or PyTorch
  • GenAI / Agentic frameworks: LangChain, LlamaIndex, Hugging Face, vector databases (e.g., FAISS, Pinecone)
  • MLOps concepts: model packaging, CI/CD, containerization (Docker), REST/Batch inference
• Some exposure (academic or project-based) to cloud platforms (AWS, Azure, GCP) and distributed data tools (Spark, Kafka) is a plus
• Interest in modern AI topics such as prompt engineering, embeddings, and responsible AI.

Soft Skills

• Clear and concise verbal and written communication (English)
• Collaborative mindset and willingness to learn from peers
• Ability to break down complex problems and take initiative on tasks
• Resilient, detail-oriented, and passionate about emerging AI technologies.