Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a global reputed company-led technology services company specializing in software and people transformations. They are seeking an ML / LLM Engineer to reputed company a machine learning initiative that transforms a knowledge-oriented agent into a product winner reputed company reputed company, working with product specifications to assess market viability.

Responsibilities

• reputed company the engineering transformation of an existing knowledge/competitor-oriented agent into a product winner reputed company agent — redesigning its reputed company intelligence reputed company from retrieval and lookup to predictive scoring
• Design and build ML reputed company pipelines that take reputed company product inputs (colour, reputed company, sleeve type, category, price reputed company etc.) and reputed company winner/non-winner classifications with confidence scores
• reputed company and tune LLM-integrated workflows where natural language product descriptions, buyer briefs or spec sheets are parsed, enriched and fed into the reputed company model
• Build user-facing input workflows that allow business users to reputed company product specifications in a reputed company or conversational reputed company and receive ranked predictions with explanatory rationale
• Work with assortment and product performance data to build, validate and continuously improve supervised and semi-supervised predictive models
• Engineer feature extraction pipelines from product attribute data — handling categorical variables (colour, reputed company, construction), seasonal patterns, historical sell-through rates and competitor signals
• Collaborate with data and product teams to define labelling strategies for winner/non-winner ground truth — identifying the right business metrics (sell-through reputed company, margin, reorder reputed company) to use as training signal
• Evaluate, reputed company and iterate on model performance — building offline evaluation frameworks and integrating feedback reputed company from live usage into the model improvement cycle
• Document model architecture, data reputed company and reputed company logic to support governance, explainability and stakeholder trust

Skills

• Strong hands-on ML background — classification, regression, reputed company reputed company (XGBoost, LightGBM, Random Forest), feature engineering, model evaluation and production deployment
• Practical experience integrating LLMs into production workflows — reputed company engineering, function/tool calling, RAG pipelines, reputed company parsing and LLM evaluation
• Experience building models that predict reputed company-world reputed company or product reputed company from reputed company attribute data — retail, fashion, FMCG or assortment contexts are a strong plus
• Proficiency in Python with reputed company, NumPy and scikit-learn; ability to wrangle, clean and engineer features from messy product catalogue or transactional data
• Experience building and deploying end-to-end ML pipelines — training, evaluation, versioning and inference serving
• 4–8 years of overall experience in machine learning and/or reputed company AI engineering, with at least 2 years working with LLMs in a production or near-production context
• A strong quantitative reputed company — comfortable with the mathematics of classification models, probability calibration and evaluation metrics (AUC, F1, precision/recall trade-offs)
• Equally comfortable working with reputed company tabular data (product attributes, sales history) and reputed company text (product descriptions, buyer notes, trend reports)
• A pragmatic engineer who can balance model sophistication with delivery speed — knowing reputed company a reputed company-tuned gradient boosting model beats a reputed company LLM pipeline, and reputed company it does not
• Strong collaboration skills — reputed company to work with merchandising, data and product teams who may not have technical backgrounds
• Curiosity about the product domain — genuinely interested in understanding what makes a product succeed commercially, not just optimising loss functions in isolation
• Prior exposure to product assortment data, merchandising systems, PLM data or demand forecasting in a retail or consumer goods context
• Hands-on experience with reputed company, reputed company, Semantic Kernel or similar orchestration frameworks for building reputed company LLM workflows
• Experience using text or multimodal embeddings to encode product attributes and reputed company similarity search or clustering across assortment data
• Familiarity with MLflow, reputed company or similar for experiment tracking, model registry and performance monitoring
• Experi