The Senior Associate AI/ML Engineer designs, builds, and deploys production-grade machine learning and multimodal AI solutions that operate across text, image, audio, and video data. The role focuses on transforming unstructured and semi-structured data into scalable AI services that power search, recommendations, automation, analytics, and content intelligence use cases.
This engineer owns model development, pipeline implementation, optimization, and deployment, while contributing to MLOps practices and mentoring junior team members.
Key Responsibilities:
1. Model & Pipeline Development:
Build and deploy multimodal ML models across:
Natural Language Processing (NLP)
Computer Vision (CV)
OCR and document understanding
Develop robust pipelines for:
Text processing, entity extraction, and classification
Image tagging, moderation, and visual understanding
Speech-to-text and speaker-level analysis
Implement Retrieval-Augmented Generation (RAG) pipelines with text and multimodal indexing.
2. Optimization & Performance Engineering
Optimize model inference for latency, throughput, and cost efficiency across batch and near real-time workloads.
Apply optimization techniques including:
Batching and asynchronous inference
Quantization, pruning, or distillation
GPU and accelerator utilization tuning
Analyze and troubleshoot model performance in production environments.
3. MLOps, LLMOps & Deployment
Build and maintain CI/CD pipelines for ML workloads using:
GitHub Actions, Azure DevOps, or Jenkins
Deploy models as cloud-native microservices, leveraging:
Docker, Kubernetes (AKS), and FastAPI
Use Azure Machine Learning for:
Experiment tracking
Model registry
Training pipelines and deployment
Implement monitoring and observability for models and pipelines:
Metrics, logging, alerts, and drift detection (e.g., Prometheus, Grafana)
4. Application & Platform Integration
Integrate AI capabilities into enterprise applications such as:
Search and recommendation systems
Knowledge, document, or content platforms
Auto-tagging, summarization, transcription, and moderation workflows
Design and expose inference and retrieval APIs for downstream consumption.
Collaborate with backend, data, and platform teams to ensure scalable and secure AI integrations.
5. Collaboration & Mentorship
Partner with product managers, data scientists, and engineers to translate business requirements into deployable AI solutions.
Review code, promote best practices, and mentor junior engineers.
Contribute to reusable components, documentation, and engineering standards.
Required Skills & Expertise
Core Technical Skills
Strong proficiency in Python with PyTorch and/or TensorFlow.
Hands-on experience with:
NLP, computer vision, or speech models
Working knowledge of LLM and orchestration frameworks:
LangChain, LlamaIndex, or equivalent
Experience with vector search and semantic retrieval:
FAISS, Pinecone, Weaviate, or Azure AI Search
Solid understanding of Docker, Kubernetes, and CI/CD pipelines.
Preferred Skills:
Experience with Azure AI and ML ecosystem, including:
Azure OpenAI
Azure Data Lake or related data services
Familiarity with real-time inference, streaming data, or distributed ML systems.