AI/ML Architect
Hiringhood · Coimbatore · onsite · Posted 26d ago
About a minute. You'll make an account on the way through.
About this role
Key Responsibilities
- Architect & Deploy ML/DL Systems
- Design and implement scalable ML pipelines: preprocessing, feature engineering, model training, evaluation, deployment.
- Engineer low-latency, high-throughput inference systems suitable for enterprise use cases.
- Domain-Centric Solutioning
- Healthcare AI: Architect HIPAA/FHIR-compliant workflows for EMR/EHR, clinical notes, imaging, and life sciences data.
- Salesforce AI: Build and integrate AI systems with Einstein 1 Platform, Apex, SOQL, Agentforce, Data Cloud, and MuleSoft APIs.
- Model Optimization & Performance
o Apply advanced ML/DL techniques: CNNs, RNNs, transformers, ensemble methods.
o Optimize models for speed, cost, and accuracy via quantization, distillation, pruning, and distributed training.
• Infrastructure & MLOps
o Build robust ML pipelines with CI/CD, Kubernetes, Docker, MLflow/Kubeflow, and Airflow.
o Deploy across AWS (SageMaker, Bedrock), Azure AI, or GCP Vertex AI with observability, telemetry, and drift detection. o Implement scalable, compliant architectures meeting HIPAA/GDPR standards.
• Future Growth into Generative AI (optional but encouraged)
o Explore LLMs, RAG, fine-tuning (LoRA, QLoRA, PEFT) and orchestration frameworks (LangChain, LlamaIndex).
o Experiment with vector databases (Pinecone, FAISS, Milvus, Weaviate) and hybrid retrieval pipelines.
• Leadership & Mentorship o Write clean, production-grade Python (PyTorch, TensorFlow, Keras).
o Mentor engineers/data scientists, enforce coding & architectural best practices.
o Translate complex business challenges into scalable AI solutions.
What You Bring
• Experience: 48+ years in AI/ML/DL engineering or architecture, with proven success delivering production-grade systems.
• Core Technical Skills:
o Expert Python with PyTorch, TensorFlow, Keras.
o Strong knowledge of ML algorithms, DL architectures (transformers, attention models, CNNs, RNNs).
o Data pipelines, embeddings, ETL/ELT, real-time streaming.
• Infrastructure:
o Cloud platforms (AWS, Azure, GCP).
o CI/CD, Kubernetes, Docker, MLOps pipelines (MLflow, Kubeflow, Airflow).
• Domain Expertise (must have one, ideally both):
o Healthcare: HIPAA/FHIR/HL7, EMR/EHR, clinical data, imaging.
\o Salesforce: Einstein 1 Platform, Agentforce, Apex/Lightning, SOQL, Data Cloud, MuleSoft.
• Nice-to-Have (Future Growth): Exposure to Generative AI/LLMs and orchestration frameworks.
• Soft Skills: Systems-level thinker, problem solver, strong communicator, collaborative leader.
Sourced from naukri