Note: The job is a remote job and is open to candidates in USA. Codvo.ai is a global empathy-led technology services company specializing in software and people transformations. They are seeking an ML / LLM Engineer to lead a machine learning initiative that transforms a knowledge-oriented agent into a product winner prediction engine, working with product specifications to assess market viability.
Responsibilities
- Lead the engineering transformation of an existing knowledge/competitor-oriented agent into a product winner prediction agent — redesigning its core intelligence layer from retrieval and lookup to predictive scoring
- Design and build ML prediction pipelines that take structured product inputs (colour, fabric, sleeve type, category, price point etc.) and output winner/non-winner classifications with confidence scores
- Develop and tune LLM-integrated workflows where natural language product descriptions, buyer briefs or spec sheets are parsed, enriched and fed into the prediction model
- Build user-facing input workflows that allow business users to enter product specifications in a structured or conversational interface 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, fabric, 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 rate, margin, reorder rate) to use as training signal
- Evaluate, benchmark and iterate on model performance — building offline evaluation frameworks and integrating feedback loops from live usage into the model improvement cycle
- Document model architecture, data lineage and prediction logic to support governance, explainability and stakeholder trust
Skills
- Strong hands-on ML background — classification, regression, ensemble methods (XGBoost, LightGBM, Random Forest), feature engineering, model evaluation and production deployment
- Practical experience integrating LLMs into production workflows — prompt engineering, function/tool calling, RAG pipelines, output parsing and LLM evaluation
- Experience building models that predict real-world commercial or product outcomes from structured attribute data — retail, fashion, FMCG or assortment contexts are a strong plus
- Proficiency in Python with pandas, 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 applied AI engineering, with at least 2 years working with LLMs in a production or near-production context
- A strong quantitative foundation — comfortable with the mathematics of classification models, probability calibration and evaluation metrics (AUC, F1, precision/recall trade-offs)
- Equally comfortable working with structured tabular data (product attributes, sales history) and unstructured text (product descriptions, buyer notes, trend reports)
- A pragmatic engineer who can balance model sophistication with delivery speed — knowing when a well-tuned gradient boosting model beats a complex LLM pipeline, and when it does not
- Strong collaboration skills — able 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 LangChain, LlamaIndex, Semantic Kernel or similar orchestration frameworks for building agentic LLM workflows
- Experience using text or multimodal embeddings to encode product attributes and perform similarity search or clustering across assortment data
- Familiarity with MLflow, Weights & Biases or similar for experiment tracking, model registry and performance monitoring
- Experience with Azure ML, AWS SageMaker or Google Vertex AI for scalable training and deployment
- Experience building agentic pipelines where LLMs orchestrate multiple tool calls, data lookups and model inference steps in sequence
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