As an AI Engineer at OmnifiCX, you will design, build, and deploy intelligent AI-driven capabilities that power our commerce platform. You will work at the intersection of machine learning, backend engineering, and product intelligence — delivering features like smart order routing, predictive analytics, conversational AI assistants, and automation pipelines. You will collaborate closely with product, engineering, and data teams to translate business problems into production-grade AI solutions.
- Design, train, and deploy machine learning models for commerce use cases such as order routing optimization, demand forecasting, fraud detection, and customer intent prediction.
- Build and maintain end-to-end ML pipelines covering data ingestion, feature engineering, model training, evaluation, and serving.
- Experiment with state-of-the-art approaches including LLMs, transformers, and classical ML algorithms depending on the problem context.
- Integrate large language models (e.g., OpenAI GPT, Anthropic Claude, open-source models via Hugging Face) into OmnifiCX product workflows such as intelligent order assistants, auto-summarization, and natural language query interfaces.
- Design prompt engineering strategies, RAG (Retrieval-Augmented Generation) pipelines, and agentic workflows for commerce-specific scenarios.
- Evaluate and benchmark LLM outputs for accuracy, latency, cost, and safety before production rollout.
- Collaborate with the OMS product team to embed AI into the order routing engine — building models that optimise routing decisions based on inventory, SLAs, cost, carrier performance, and real-time signals.
- Develop rule-augmented ML models that work alongside deterministic business logic in the routing module.
- Monitor model performance in production and implement feedback loops for continuous improvement.
- Build and maintain data pipelines for structured and unstructured commerce data
- Work with data and platform teams to define feature stores, data schemas, and batch/streaming data flows for model training and inference.
- Ensure data quality, lineage, and reproducibility across ML experiments.
- Work closely with product managers, backend engineers, and business analysts to scope and deliver AI features aligned with OmnifiCX roadmap priorities.
- Mentor junior engineers on AI/ML best practices and promote a culture of data-driven decision making.
- Document AI system designs, model cards, and experiment outcomes for cross-functional transparency.
- Experience: 4–8 years in AI/ML engineering, with at least 2 years delivering production ML systems.
- Machine Learning: Hands-on experience with supervised, unsupervised, and reinforcement learning. Proficiency in scikit-learn, XGBoost, LightGBM, and deep learning frameworks (PyTorch or TensorFlow).
- LLMs & Generative AI: Practical experience integrating LLM APIs (OpenAI, Anthropic, Cohere, or open-source). Familiar with LangChain, LlamaIndex, prompt engineering, and RAG pipeline design.
- Programming: Strong Python skills. Proficiency in Pandas, NumPy, and ML experimentation tooling.
- Data Engineering: Experience building pipelines using Apache Spark, Airflow, or dbt. Comfortable with SQL and large structured datasets.
- Model Serving & APIs: Experience deploying ML models as REST/gRPC microservices using FastAPI or Flask.
- MLOps: Experience with model monitoring, versioning, and CI/CD for ML pipelines.
- Cloud Platforms: Hands-on experience with AWS or GCP/Azure equivalents.
- Optimization Problems: Ability to frame business problems as optimization or ranking tasks.
- Collaboration Tools: Familiarity with Jira, Confluence, or similar tools.
- Certifications: AWS Certified Machine Learning – Specialty, Google Professional ML Engineer, or equivalent.
- Commerce / OMS Domain: Exposure to e-commerce, order management, supply chain, or logistics AI use cases.
- Vector Databases: Experience with Pinecone, Weaviate, Chroma, or pgvector.
- Agentic AI: Experience designing multi-step agentic workflows.
- Streaming Data: Exposure to Kafka or Kinesis.
- B.Tech / B.E. or M.Tech in Computer Science, Data Science, Mathematics, or a related field.
- Strong analytical thinking.
- Excellent communication skills.
- Ability to thrive in a fast-paced environment.
- Demonstrated track record of taking ML projects from prototype to production.
- Work on high-impact AI problems.
- Career growth opportunities.
- Mentorship and coaching.
- Competitive benefits.
- Flexible work options.
