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

Roles Responsibilities: AI/ML Model Development:

- 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

LLM Generative AI Integration:
- Integrate large language models (eg, 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

AI-Powered Order Routing Optimization:
- 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

Data Engineering Feature Pipelines:
- 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

Collaboration Mentorship:
- 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 and Skills: Minimum:
- 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

Preferred:
- Certifications: AWS Certified Machine Learning Specialty, Google Qualified 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

Minimum Qualifications:
- 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

Job Overview Work Experience 4-8 Years

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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