Immediate Start: Senior Ai/aiops Engineer

Job Category Product Development We are seeking a skilled professional to design develop and deploy scalable machine learning solutions The ideal candidate will have a strong foundation in data science and applied machine learning with experience in building agentic workflows using tools like RAG and NL-to-SQL They will own the full lifecycle of machine learning solution development ensuring enterprise security privacy and compliance standards are met Strong communication and collaboration skills are required The candidate will play a critical role in driving the organization s success by developing machine learning solutions that meet business needs and drive results Please apply If you have the passion and standard methodologies work in an environment where challenges are a norm where individual brilliance is valued and goes hand in hand with team performance Where being proactive is how we do things Responsibilities Key responsibilities AI ML Design train and optimize machine learning models for real-world applications Build end-to-end ML pipelines including data preprocessing feature engineering model training validation and deployment Collaborate with data engineers and software developers to integrate ML models into production systems Monitor model performance detect data drifts and retrain models for continuous improvement GenAI Agentic Solution design and orchestration Architect LLM-powered applications including intent routing across tools skills Implement agentic workflows using frameworks such as LangGraph or equivalents decompose tasks manage tool invocation and ensure determinism guardrails Integrate MCP-compatible tools and services to extend system capabilities Retrieval and embeddings Build effective RAG systems chunking strategies embedding model selection vector indexing reranking and grounding to authoritative data Optimize vector stores and search ANN hybrid filters metadata schemas Prompting and model strategy Develop robust prompting patterns and templates structure prompts for tool use and function calling Compare generic vs fine-tuned LLMs for intent routing make data-driven choices on cost latency accuracy and maintainability Data and integrations Implement NL2SQL and guarded SQL execution patterns connect to microservices and enterprise systems via secure APIs Define and enforce data schemas metadata and lineage for reliable retrieval Production readiness Establish evaluation datasets and automated regressions for RAG and agents Monitor quality precision recall hallucination rate latency cost and safety Apply guardrails PII handling access controls and policy enforcement end-to-end MLOps LangOps Version prompts models embeddings and pipelines manage A B tests and rollout Instrument tracing telemetry for agent steps and tool calls implement fallback timeout retry policies Core qualifications Programming Strong proficiency in Python NumPy Pandas Scikit-learn experience with ML frameworks TensorFlow PyTorch Machine Learning Deep Learning Hands-on experience with supervised unsupervised and reinforcement learning techniques Mathematics Statistics Solid foundation in linear algebra probability optimization and statistical modeling Data Handling Experience with SQL and NoSQL databases data preprocessing and feature engineering GenAI Strong understanding of vector embeddings and similarity search cosine IP L2 chunking strategies and reranking Hands-on experience building RAG pipelines indexing metadata hybrid search evaluators Practical prompt engineering for tool use function calling and agent planning Experience with agentic frameworks e g LangGraph or similar and orchestrating tools services familiarity with MCP and tool integration patterns Knowledge of NL2SQL techniques SQL safety schema constraints query sandboxes and microservice integration Ability to evaluate tradeoffs generic base LLMs vs fine-tuned task-specific models accuracy drift data ops burden latency cost Proficiency with Python and common LLM RAG libraries containerization and CI CD Understanding of enterprise security privacy and compliance RBAC ABAC for data access logging and auditability MLOps Deployment Familiarity with model deployment frameworks MLflow Kubeflow SageMaker Vertex AI CI CD pipelines and containerization Docker Kubernetes Preferred experience Hands-on experience with at least one major cloud provider AWS Azure GCP OCI Experience with large-scale distributed systems and big data frameworks Spark Hadoop Retrieval optimization hybrid lexical vector metadata filtering learned rerankers Model fine tuning adapter methods LoRA SFT DPO and evaluation Observability stacks for LLM apps tracing eval dashboards cost latency SLOs Document AI OCR layout parsing and schema construction for unstructured data Caching batching and KV-cache considerations for throughput cost Safe tool-use patterns constrained decoding JSON schemas policy checks Qualifications Career Level - 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