Job Title: AI/ML Engineer Senior Associate Level
Experience: 6+ years
Industry: Any
Role SummaryThe 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 Responsibilities1. Model & Pipeline Development Build and deploy multimodal ML models across:o Natural Language Processing (NLP)o Computer Vision (CV)o OCR and document understanding Develop robust pipelines for:o Text processing, entity extraction, and classificationo Image tagging, moderation, and visual understandingo 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:o Batching and asynchronous inferenceo Quantization, pruning, or distillationo 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:o GitHub Actions, Azure DevOps, or Jenkins Deploy models as cloud-native microservices, leveraging:o Docker, Kubernetes (AKS) and FastAPI Use Azure Machine Learning for:o Experiment trackingo Model registryo Training pipelines and deployment Implement monitoring and observability for models and pipelines:o Metrics, logging, alerts, and drift detection (e.g., Prometheus, Grafana)
4. Application & Platform Integration Integrate AI capabilities into enterprise applications such as:o Search and recommendation systemso Knowledge, document, or content platformso 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 & ExpertiseCore Technical Skills Strong proficiency in Python with PyTorch and/or TensorFlow. Hands-on experience with:o NLP, computer vision or speech models Working knowledge of LLM and orchestration frameworks:o LangChain, LlamaIndex or equivalent Experience with vector search and semantic retrieval:o 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.
Qualifications 6+ years of hands-on experience in ML engineering or applied AI roles. Bachelors degree in computer science, AI, Engineering, or related field.
Deal Breaker Skill
Python
Mandatory Skills
Python
| Natural Language Processing (NLP)
| Python
