AI / ML Engineer

 About The Role  

Project Role :AI / ML Engineer

Project Role Description :Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills :Machine Learning Operations

Good to have skills :Microsoft Azure Machine Learning
Minimum 5 year(s) of experience is required

Educational Qualification :15 years full time education
About The Role :MLOps EngineerRole SummaryOwn the infrastructure and pipelines that take data, models, prompts, and agents from development to reliable, observable production. Provide CI/CD, model/prompt registries, evaluation/monitoring, search/index operations, and cost governance. Should have prior experience in deploying models for real-time / batch inferencing methods.ResponsibilitiesImplement CI/CD for data, models, prompts, and agents (blue/green, canary); artifact/version management and rollbacks.Operate retrieval infrastructure:Elastic/OpenSearch and vector indexes (FAISS/Pinecone/pgvector); backups, synonyms, compaction.Build and maintain ingestion/extraction pipelines (Airflow/Prefect; Trafilatura/Playwright/Selenium; pypdf/pdfplumber/ocrmypdf).Wire up evaluation/observability:quality/drift/hallucination/safety/latency/token-cost dashboards and alerts; integrate DeepEval hooks.Containerize and serve workloads (Docker/Kubernetes); infrastructure-as-code (Terraform on Azure).Security and governance:secrets management, access control, publish runbooks and SLOs; incident response.ML Ops Stack (Tooling Familiarity)CI/CD:GitHub Actions/Argo CD; artifact registries.Experiment tracking & registries:MLflow/Weights & Biases; prompt/model versioning.Pipelines & queues:Airflow/Prefect; SQS for messaging.Observability:Prometheus/Grafana/OpenTelemetry/CloudWatch; ELK where applicable.Serving & optimization:Docker/Kubernetes; acceleration/optimization paths (e.g., DeepSpeed where appropriate).Search & embeddings:Elastic/OpenSearch; vector DBs; docarray for embedding flows.Tokenization & preprocessing:tiktoken; robust HTML/text/PDF tooling.
QualificationsHands-on experience operating ML/LLM systems in production with strong automation discipline.Clear documentation and collaboration skills; comfort working in Agile environments.You will be working with a Trusted Tax Technology Leader, committed to delivering reliable and innovative solutions Qualification 15 years full time education