- Company Name
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- Job Title
- Sr. Machine Learning Engineer
- Job Description
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**Job Title:** Sr. Machine Learning Engineer
**Role Summary:**
Design, develop, and deploy scalable machine learning systems that power personalized AI‑enhanced experiences. Lead the creation of production‑ready ML pipelines, orchestrate data workflows, and innovate with large language models (LLMs) and generative AI to enable high‑impact features for a global SaaS product.
**Expectations:**
- End‑to‑end ownership of ML model lifecycle from research to production.
- Proven ability to operationalize complex models at scale on cloud infrastructure.
- Strong collaboration with cross‑functional teams (Product, Engineering, Data Engineering, Analytics).
- Continuous learning and adoption of emerging AI research and tooling.
**Key Responsibilities:**
- Design, train, and deploy ML models and workflows using Docker, Kubernetes, TensorFlow/PyTorch, and LangChain.
- Build and maintain production pipelines with Airflow, MLflow, and CI/CD tools (ArgoCD, Jenkins, Terraform).
- Integrate vector databases and Kafka for high‑volume, real‑time data processing.
- Implement LLM‑based generative AI features; evaluate and benchmark performance, fairness, and reliability.
- Create automated evaluation pipelines and model monitoring dashboards with Datadog/OpenSearch.
- Collaborate on product requirements, deliverables, and technical roadmaps.
- Mentor and guide junior engineers in ML best practices.
**Required Skills:**
- 4+ years of production ML engineering experience.
- Proficiency in Python, PyTorch, LangChain, Agents, and FastAPI or Faust.
- Cloud: AWS (SageMaker, Bedrock, Lambda, ECS, EKS), Docker, Kubernetes.
- CI/CD: ArgoCD, Jenkins, Terraform.
- Data streaming: Kafka, vector databases (e.g., Pinecone, Milvus).
- ML lifecycle: MLflow, experiment tracking, model registry.
- Monitoring: Datadog, OpenSearch, or equivalent.
- Generative AI and LLMs: fine‑tuning, inference, RAG systems.
- Strong communication skills; ability to explain complex concepts to non‑technical stakeholders.
**Required Education & Certifications:**
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Data Science, or related field.
- No mandatory certifications required; relevant Cloud or ML certifications (e.g., AWS Certified Machine Learning – Specialty, TensorFlow Developer) are advantageous.