Hiring for client Key Responsibilities & Accountabilities AI Strategy & Roadmap Define and execute the enterprise AI strategy and roadmap aligned with business objectives and digital transformation goals. Identify high-impact AI/ML use cases across functions (forecasting, optimization, automation, personalization, fraud detection, predictive maintenance, etc.). Prioritize initiatives based on business value, feasibility, and ROI. AI Solution Development & Deployment Lead end-to-end AI solution lifecycle: problem definition, data preparation, feature engineering, model development, testing, deployment, and monitoring. Design and implement ML, Deep Learning, NLP, Computer Vision, and Generative AI solutions using industry-standard frameworks and libraries (e.g., Python, TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM). Hands-on experience with NLP toolkits and transformer models (Hugging Face Transformers, spaCy, BERT, GPT-family, LLaMA), embeddings, prompt engineering, and retrieval-augmented generation (RAG) for LLM-based solutions. Computer vision experience with OpenCV, Detectron2, EfficientDet, YOLO and CNN/transformer-based architectures for image/video tasks. Ensure seamless integration of AI models with ERP, CRM, WMS, OMS, e-commerce platforms, data warehouses and expose ML capabilities via REST/gRPC APIs or microservices. Champion production readiness: containerization (Docker), orchestration (Kubernetes), API design, load testing, latency optimization, and edge/ONNX/TensorRT deployments where applicable. Data, MLOps & Platforms Define AI architecture including data pipelines, model training environments, cloud platforms, feature stores, and MLOps frameworks. Provide clear patterns for training, validation and production serving. Data engineering and pipeline examples: Apache Spark (batch), Kafka / Debezium (streaming), Flink (stream processing), Airflow / Argo Workflows (orchestration), and ETL/ELT with tools like dbt. Feature store and data quality: Feast, Tecton; data validation and testing with Great Expectations and data monitors. MLOps platforms & practices (explicit examples): experiment tracking (Weights & Biases, MLflow, Neptune), model registries (MLflow Registry, DVC), CI/CD for ML (Jenkins, GitHub Actions, GitLab CI, Tekton), workflow orchestration (Kubeflow Pipelines, Argo, Prefect), and reproducible pipelines (Pachyderm). Model serving & inference: KServe, Seldon Core, BentoML, TorchServe, TensorFlow Serving; low-latency inference optimizations using ONNX, TensorRT, NVIDIA Triton, and edge deployments where needed. Distributed training & acceleration: PyTorch DDP, Horovod, DeepSpeed, TensorFlow MultiWorker; GPU/TPU provisioning and management on cloud or on-prem clusters. Cloud AI platforms & infra automation: AWS (SageMaker, EKS, ECS, S3, RDS), GCP (Vertex AI, GKE, BigQuery), Azure (Azure ML, AKS); infrastructure as code using Terraform, CloudFormation, Helm charts. Observability, monitoring & model governance: Prometheus, Grafana, ELK stack, Evidently, WhyLabs, Fiddler for model monitoring, drift detection, data quality alerts, and automated retraining triggers. Vector databases & retrieval stores for embeddings: Pinecone, Milvus, Weaviate, Qdrant; typical use with RAG pipelines and semantic search architectures. Security, compliance & cost optimization: secrets management (HashiCorp Vault, cloud KMS), network policies, IAM best practices, and cost monitoring/rightsizing for GPUs & storage. Typical production ML workflow (example pattern): data ingestion (Kafka) -> ETL & feature engineering (Spark + dbt) -> experiments (PyTorch/TensorFlow + W&B) -> model registry (MLflow) -> CI/CD (GitHub Actions + Argo) -> serving (Seldon/BentoML on Kubernetes) -> monitoring (Prometheus + Evidently) -> retraining pipeline (Airflow/Kubeflow). Business Enablement & Adoption Partner with business leaders to translate business problems into AI-driven solutions. Drive AI adoption and change management, ensuring users trust and effectively use AI outputs. Enable AI-powered dashboards, decision-support tools, and automation systems. Governance, Ethics & Compliance Establish AI governance frameworks, including data privacy, explainability, bias mitigation, and ethical AI principles. Ensure compliance with data protection laws, healthcare/pharma regulations, and ISO standards. Work closely with Information Security teams to ensure secure and compliant AI implementations. Team Leadership & Capability Building Build and lead high-performing AI, Data Science, and ML Engineering teams (onshore/offshore). Define skill frameworks, hiring plans, training programs, and succession planning. Foster a culture of innovation, experimentation, and continuous learning. Vendor & Partner Management Evaluate and manage AI vendors, platforms, cloud providers, and system integrators. Negotiate SLAs, performance metrics, and commercial terms. Ensure optimal cost-to-value outcomes for AI investments. Key Performance Indicators (KPIs) Number of AI use cases deployed to production Business value and ROI delivered through AI initiatives Model accuracy, stability, and adoption rates Time-to-deploy AI solutions Compliance, security, and audit outcomes AI platform scalability and cost optimization Qualifications Required Qualifications (Must-haves) Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field. 10+ years of overall professional experience, including a minimum of 6 years in AI/ML leadership roles managing cross-functional teams and delivering enterprise solutions. Proven track record of designing and implementing enterprise-scale AI/ML solutions that delivered measurable business impact (examples: forecasting, personalization, automation, predictive maintenance, fraud detection). Strong hands-on experience with Python and SQL and working knowledge of ML/DL frameworks such as TensorFlow or PyTorch and scikit-learn. Operational experience deploying models to production using containerization and orchestration (Docker, Kubernetes) and exposing models via APIs/microservices. Familiarity with at least one major cloud provider and its ML services (AWS/GCP/Azure) and practical experience with model training and inference on cloud infrastructure. Demonstrated expertise in MLOps practices (CI/CD for ML, model versioning/registry, experiment tracking, monitoring) and the ability to implement robust production pipelines. Strong software engineering discipline: version control (Git), testing, code review practices, and API design. Proven ability to translate business problems into AI solutions and to communicate with senior stakeholders and cross-functional teams. Preferred Qualifications (Optional / Nice-to-have) Master’s degree or PhD in AI, Data Science, Machine Learning, or Analytics. Experience with big data and data engineering technologies (Apache Spark, Kafka, Flink), cloud data warehouses (Snowflake, Redshift, BigQuery) and orchestration tools (Airflow). Hands-on familiarity with MLOps/platform tooling: MLflow, DVC, Kubeflow, Argo, Seldon, WhyLabs; infrastructure automation with Terraform and Helm. Experience with advanced ML topics: distributed training (GPU/TPU), ONNX/TensorRT optimizations, latency and cost optimization techniques. Practical experience with NLP/LLMs (Hugging Face, transformer models), embeddings, RAG architectures, vector databases (Milvus, Pinecone, Weaviate) and prompt engineering for production use cases. Experience in computer vision solutions (OpenCV, Detectron2, YOLO, ViT) where applicable. Knowledge of model interpretability and governance tools/methods (SHAP, LIME), bias detection & mitigation approaches, and privacy-preserving techniques (differential privacy, anonymization). Domain experience in healthcare/pharma, retail, manufacturing, BFSI, or supply chain is a plus. Track record of publications, patents, or speaking at conferences is advantageous. Technical Skills & Keywords [This section highlights concrete technical keywords and grouped tooling for search and candidate matching — examples provided to improve discoverability and clarify expected stacks] Languages & Core: Python (primary), SQL, Bash; secondary: R, Scala, Java ML / DL Frameworks: PyTorch, TensorFlow, JAX, scikit-learn, XGBoost, LightGBM, CatBoost NLP & LLMs: Hugging Face Transformers, spaCy, BERT, GPT-family, LLaMA, SentenceTransformers, tokenizers, prompt engineering, RAG patterns Computer Vision: OpenCV, Detectron2, YOLO family, EfficientDet, Vision Transformers (ViT) Data & Big Data: Apache Spark, Kafka, Flink, Hive, Presto, dbt; cloud warehouses: Snowflake, Redshift, BigQuery Feature Stores & Data Quality: Feast, Tecton, Great Expectations MLOps & Experimentation: MLflow, Weights & Biases, Neptune, DVC, Guild; experiment tracking and model registry tools Orchestration & Pipelines: Airflow, Kubeflow Pipelines, Argo Workflows, Prefect Model Serving & Inference Platforms: Seldon Core, KServe, BentoML, TorchServe, TensorFlow Serving, NVIDIA Triton Vector & Embedding Stores: Pinecone, Milvus, Weaviate, Qdrant Containerization & Orchestration: Docker, Kubernetes (EKS/GKE/AKS), Helm Infra Automation & CI/CD: Terraform, CloudFormation, Jenkins, GitHub Actions, GitLab CI, Tekton Distributed Training & Acceleration: PyTorch DDP, Horovod, DeepSpeed; GPU/TPU management Optimization & Model Formats: ONNX, TensorRT, quantization, pruning Monitoring & Observability: Prometheus, Grafana, ELK, Evidently, WhyLabs, Fiddler Datastores & Caching: PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch Security & Governance: HashiCorp Vault, cloud KMS, IAM best practices, privacy-preserving tooling Testing & Quality: pytest, tox, Great Expectations for data, integration & end-to-end ML tests Example typical production ML stack (concise): Python + PyTorch/TensorFlow, feature store (Feast), experiment tracking (W&B/MLflow), model registry (MLflow), CI/CD (GitHub Actions + Argo), serving (Seldon/BentoML on Kubernetes), observability (Prometheus + Grafana + Evidently), vector DB (Pinecone) for RAG scenarios. Desired Competencies Strong business-first mindset with ability to translate AI into measurable outcomes Strategic thinking with hands-on technical depth Excellent stakeholder management and executive communication High ethical standards and governance orientation Ability to operate in fast-paced, transformation-led environments Skills: platforms,ml,intelligence,artificial intelligence

About Quickhyre AI

QuickHyre AI is an online job portal and HRMS software platform for hiring and workforce management, trusted by over 10 million active users. It connects freshers and experienced professionals across all career levels with verified job opportunities, while enabling companies to hire, onboard, and manage employees through a single system. Candidates use QuickHyre AI to search and apply for jobs across entry-level, mid-level, and senior roles, including internships and contract positions across tech and non-tech domains. Employers use QuickHyre AI as a hiring platform, applicant tracking system (ATS), and HRMS to post jobs, screen candidates, manage applications, onboard hires, and maintain employee records. Built for startups, growing companies, and enterprises, QuickHyre AI reduces irrelevant applications, improves hiring efficiency, and simplifies HR operations. By combining job discovery, online recruitment, and HR management software, QuickHyre AI supports end-to-end talent acquisition and workforce administration at scale.