Senior Data scientist/AIMl Engineer-Aziro

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Role Summary

Senior Artificial Intelligence & Machine Learning (AI/ML) Engineer

This role focuses on individual technical contribution and requires close collaboration with solution architects, AIML leads, and fellow engineers to translate business use cases into scalable, secure, cloud-native AI services.

The ideal candidate will bring deep technical expertise across the AI/ML lifecyclefrom prototyping to deploymentwhile contributing to a culture of engineering excellence through peer reviews, documentation, and platform innovation. They will play a critical role in delivering robust, high-performance AI systems in partnership with the broader AI/ML team.  

Key Responsibilities

Model Development & Optimization

• Fine-tune foundation models (e.g., GPT-4, Llama 3).
• Implement prompt engineering and basic parameter-efficient tuning (e.g., LoRA).
• Conduct model evaluation for quality, bias, and hallucination; analyze results and suggest improvements.

RAG & Agentic Systems (Exposure, Not Ownership)

• Assist in building RAG pipelines: Participate in integrating and embedding generation, vector stores (e.g., FAISS, pgvector), and retrieval/ranking components.
• Work with multi-agent frameworks (e.g., LangChain, Crew AI)  

Production Engineering / MLOps

• Contribute to CI/CD pipelines for model training and deployment (e.g., GitHub Actions, SageMaker Pipelines).
• Help automate monitoring for latency, drift, and cost; assist in lineage tracking (e.g., MLflow).
• Containerize services with Docker and assist in orchestration (e.g., Kubernetes/EKS/GKE)  

Data & Feature Engineering

• Build and maintain data pipelines for collection, cleansing, and feature generation (e.g., Airflow, Spark).
• Implement basic data versioning and assist with synthetic data generation as needed

Code Quality & Collaboration

• Participate in design and code reviews.
• Contribute to testing (unit, integration, guardrail/hallucination tests) and documentation.
• Share knowledge through sample notebooks and internal sessions

Security, Compliance, Performance

• Follow secure coding and Responsible AI guidelines.
• Assist in optimizing inference throughput and cost (e.g., quantization, batching) under guidance.
• Ensure SLAs are met and contribute to system auditability

Technology Stack

Programming Languages & Frameworks

• Python (expert)
• JavaScript/Go/TypeScript (nice-to-have)
• Strong knowledge of libraries such as Scikit-learn, Pandas, NumPy, XGBoost, LightGBM, TensorFlow, PyTorch.
• PyTorch, TensorFlow/Keras, Hugging Face Transformers/PEFT, LangChain/LlamaIndex, Ray/PyTorch Lightning, FastAPI/Flask
• Experience working with RESTful APIs, authentication (OAuth, API keys), and pagination

Cloud & DevOps

• Expertise in one or more cloud vendors like AWS, GCP, Azure
• Containers (Docker), Orchestration (Kubernetes, EKS/GKE/AKS)
• MLOps

Databases

• Relational: PostgreSQL, MySQL
• NoSQL: MongoDB / DynamoDB
• Vector Stores: FAISS / pgvector / Pinecone / OpenSearch / Milvus / Weaviate

RAG Components

• Document loaders/parsers, text splitters (recursive/semantic), embeddings (OpenAI, Cohere, Vertex AI), hybrid/BM25 retrievers, rerankers (Cross-Encoder)

Multi-Agent Frameworks

• Crew AI / AutoGen / LangGraph / MetaGPT / Haystack Agents, planning & tool-use patterns

Testing & Quality

• Unit/integration testing (pytest), guardrails 

Qualifications:

1012 years of experience in software engineering/data science, with 4+ years leading AI/ML projects end-to-end.

Bachelors or Masters in Computer Science, Artificial Intelligence, Data Science, or related field.

Certifications preferred: AWS Certified Machine Learning / Google Professional Machine Learning Engineer / Azure AI Engineer Associate and Kubernetes CKA/CKAD.

Experience in regulated industries (Fintech, Healthcare, eCommerce) is a plus.

Soft Skills & Leadership Attributes

Influential communication: Translate complex ML concepts for non-technical stakeholders; strong presentation & storytelling.

Mentorship mindset: Coach, upskill, and inspire cross-functional teams; foster psychological safety.

Ownership & bias for action: Deliver POCs, iterate based on feedback, drive solutions to production.

Critical thinking & experimentation: Data-driven decision making, hypothesis testing, A/B experimentation.

Adaptability: Stay current with rapid advances in GenAI, tooling, and research; evaluate emerging models/services.