Machine Learning Analyst

Purpose & Overall Relevance for the OrganizationA 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 EngineeringSupport 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 monitoringWork with data scientists to productionize models and ensure business value is consistently deliveredContribute to model observability — logging, drift tracking, performance dashboardsGenAI & Agentic AIUse LLMs, prompt engineering, embeddings, and vector stores to enable intelligent applicationsBuild small-scale AI agents using frameworks like LangChain, LlamaIndex, or equivalentExperiment with responsible and explainable use of foundation models to solve clear business problemsAnalyticsAssist in applying machine learning techniques with guidance from senior engineers or data scientists.Perform exploratory data analysis and support feature selection and data preparationUse unsupervised learning when appropriate for early insights or pattern discoveryData Management & EngineeringSupport creation, improvement, and validation of curated datasets for ML applicationsContribute to data quality checks, schema design, and efficient feature retrievalFollow best practices for security, accessibility, and ethical use of data.Programming / Software DevelopmentWrite clean, reliable, well-tested code (primarily in Python)Implement and maintain CI/CD workflows for ML components with supervisionDeploy ML workloads on cloud or on-prem environments using modern tooling.Visualization & StorytellingBuild automated dashboards to support model/data health visibilityCommunicate insights clearly to technical and non-technical stakeholders.Testing & ReliabilityContribute to writing unit, integration, and regression tests for ML componentsMonitor test outcomes and support issue resolution.Education & Experience — Minimum Qualifications1+ 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 practicesFamiliarity with:ML frameworks: scikit-learn, TensorFlow, or PyTorchGenAI / Agentic frameworks: LangChain, LlamaIndex, Hugging Face, vector databases (e.g., FAISS, Pinecone)MLOps concepts: model packaging, CI/CD, containerization (Docker), REST/Batch inferenceSome exposure (academic or project-based) to cloud platforms (AWS, Azure, GCP) and distributed data tools (Spark, Kafka) is a plusInterest in modern AI topics such as prompt engineering, embeddings, and responsible AI.Soft SkillsClear and concise verbal and written communication (English)Collaborative mindset and willingness to learn from peersAbility to break down complex problems and take initiative on tasksResilient, detail-oriented, and passionate about emerging AI technologies.