MLOps Engineer

Mphasis

• $120K — $150K *
New York, NY 10025In-Person
Information Technology
Less than 5 years of experience
Today
Be an Early Applicant
Job Overview by Ladders

Qualifications

  • 5-7 years of experience in building production AI/ML systems at scale.
  • Proficient in ML/AI frameworks like TensorFlow and PyTorch.
  • Strong coding skills in Python and familiarity with Java/C/C++.
  • Experience with cloud services including AWS and GCP.
  • Knowledge of databases like PostgreSQL and MongoDB.
  • Familiarity with deploying real-time ML inference pipelines.
  • Hands-on experience with event-driven platforms like Apache Kafka.

Responsibilities

  • Develop and implement end-to-end MLOps capabilities.
  • Build and maintain real-time ML inference pipelines.
  • Monitor and adjust for model and feature drift.
  • Utilize cloud resources effectively with AWS and GCP.
  • Collaborate with data engineering teams to optimize data flows.
  • Create and manage CI/CD pipelines for ML model deployments.
  • Engage in prompt engineering and orchestration of agentic systems.

Benefits

  • Opportunity to work with cutting-edge AI/ML technologies.
  • Flexible working environment with potential remote options.
  • Professional growth with access to new tools and frameworks.
  • Supportive team culture fostering collaboration and innovation.
  • Health and wellness programs available for employees.
Full Job Description
Role description

Job Title - MLOps Engineer

Location - Charlotte NC

Technical Expertise
ML/Al Frameworks: PyTorch, TensorFlow, JAX, HuggingFace, LangChain, LangGraph, Llamalndex, DSPy, ONNX Runtime, TensorRT GenAl & LLMs: GPT-4/Claude API, LoRA/QLoRA fine-tuning, RAG (FAISS, Pinecone, ChromaDB), prompt engineering, agentic orchestration Languages: Python, C/C++, Java, SQL, Scala, GoLang, JavaScript
Cloud & Infra: AWS (SageMaker, S3, Lambda), GCP (GKE, Vertex Al, BigQuery), Kubernetes, Terraform, Docker Databases: PostgreSQL, MySQL, MongoDB, Neo4j, BigQuery, Pinecone, ChromaDB, Redis Libraries: Pandas, NumPy, Scikit-learn, OpenCV, Keras, Spark, Kafka Dev Tools: Linux, Git, Docker, Kubernetes, Jenkins
Experience Required
• Experience building production Al/ML systems at scale
• Deploying real-time ML inference pipelines processing millions of records at high throughput
Experience building end to end automated MLOps capabilities along with model and feature drift monitoring
• Experience with event-driven and streaming platforms such as Apache Kafka,
* Ladders Estimates

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