Senior Machine Learning Engineer

Role Overview

We seek an accomplished Senior Machine Learning Engineer with robust software engineering expertise to embed Machine Learning and generative AI functionalities within scalable, production-level applications. This position is suited for a seasoned engineer with over a decade of experience, passionate about developing comprehensive systems—from front-end interfaces and back-end servers to APIs and ML-driven services—rather than focusing on research.

Core Responsibilities

• Collaborate closely with machine learning researchers, data scientists, and platform teams to convert prototypes, retrieval-augmented generation (RAG) pipelines, and multi-agent workflows into dependable systems deployed enterprise-wide.
• Develop end-to-end full-stack solutions incorporating ML/GenAI services.
• Design and implement scalable, secure platforms supporting RAG, model serving, and agentic applications.
• Enhance engineering efficiency by crafting reusable components, libraries, and frameworks for ML integration.
• Provide architectural guidance and code-level best practices for ML-aware software development.
• Communicate complex architectural decisions and technical concepts effectively to both technical and non-technical stakeholders.

Professional Qualifications & Skills

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical discipline.
• Minimum of 10 years in software engineering roles focused on full-stack, distributed systems, APIs, and cloud environments.
• 4 to 6 years of hands-on experience working with machine learning or generative AI solutions in production settings.
• Proven experience designing and deploying RAG pipelines using embeddings and vector search methods.
• Practical knowledge of vector databases such as FAISS, Pinecone, Weaviate, or Milvus.
• Familiarity with agentic system frameworks like LangChain, CrewAI, LangGraph, and AutoGen.
• Expertise in cloud-native development, preferably with AWS services including S3, Lambda, ECS/EKS, SageMaker, and API Gateway.
• Advanced proficiency in back-end programming with Python, supplemented by experience in Node.js, Java, C#, or Go.
• Competence in modern front-end frameworks such as React or Angular to build durable web applications.
• Strong database skills across relational databases (Oracle), cloud data warehouses (Snowflake), vector databases, and knowledge graphs (e.g., Neo4j, RDF/SPARQL).
• Experience with DevOps practices incorporating CI/CD pipelines, Docker, Kubernetes, GitHub Actions, and infrastructure as code.
• Understanding of Responsible AI principles, data privacy, and ethics related to ML-powered systems.
• Valued additional capabilities include model monitoring, evaluation frameworks, and automated alerting mechanisms.

Expertise and Engineering Skills

• End-to-end system engineering integrating machine learning and generative AI components.
• Proficiency in converting research notebooks and prototypes into production-ready APIs and microservices.
• Strong grasp of system design including distributed architectures, container orchestration, scaling, observability, and resilience.
• Knowledgeable in selecting appropriate ML model types for embedding into business workflows.
• Experience deploying and managing multi-agent agentic workflows with production monitoring.
• Hands-on skills with observability tools like Prometheus, Grafana, OpenTelemetry, and logging/metrics pipelines.
• High engineering standards with focus on CI/CD, testing, version control, tracing, secure coding, and canary/blue-green deployments.
• Capability to assess and adopt emerging GenAI frameworks and tools for practical business use.
• Strong collaboration, mentoring, and communication skills to support cross-functional project delivery.

Value Addition

• Deliver comprehensive full-stack components enabling ML and generative AI capabilities within business applications.
• Create robust, scalable, and secure platforms supporting RAG, models, and agentic applications.
• Boost productivity through reusable engineering assets for ML service integration.
• Advise on alignment of various model types such as large language models, embeddings, and classifiers with business challenges.
• Engage closely with research, product, and cloud teams to generate tangible business outcomes.
• Promote best practices in architecture, software development, and ML-focused engineering methodologies.