Senior AIML Engineers (6+ Years)

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 lifecycle—from prototyping to deployment—while 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

• 7–10years

  total software/ML engineering experience, including

  3+years

  delivering ML models or GenAI systems to production.
• Proven track record building and optimising

  RAG or LLMpowered

  applications at scale
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow) and in cloudnative deployment (AWS/GCP/Azure).
• Handson experience with vector databases, embeddings, and prompt engineering.
• Experience in regulated industries (Fintech, Healthcare, eCommerce) is a plus.
• Experience with

  multiagent

  frameworks (CrewAI, AutoGen, LangGraph).
• Certifications such as AWS CertifiedMachineLearning  Specialty / AzureAIEngineer / GoogleProfessionalMachineLearningEngineer.
• Bachelor’s degree in Computer Science, Data Science, Engineering or related discipline (Master’s preferred).

Soft Skills & Leadership Attributes

• Ownership mindset:

  drives features from design through deployment and monitoring.

• Clear communicator:

  explains technical tradeoffs to stakeholders, writes concise docs, and updates project artefacts.

• Collaboration & mentorship:

  pairs with junior engineers, shares knowledge in brownbag sessions, gives constructive PR feedback.

• Continuous learning:

  tracks latest GenAI research, evaluates new tooling, and proposes incremental improvements.