Job Summary
As a Mid-Level Machine Learning Application Engineer, you will play a critical role in building, integrating, and optimizing machine learning components, especially around LLMs, NLP, and conversational systems. You’ll take ownership of components in the AI application stack and contribute meaningfully to product and architecture decisions. This role requires a balance of software engineering, ML understanding, and the ability to work both independently and collaboratively.
Key Responsibilities
- ML Application Architecture Implementation: Design and implement ML-powered features and pipelines using frameworks such as Langchain, LlamaIndex, and related tools.
- Conversational Framework Development: Build and manage conversational flows, dialog management logic, and context handling in AI systems.
- System Integration and Optimization: Integrate ML modules (e.g., RAG pipelines, inference services) into larger systems, optimizing for latency, performance, and scalability.
- Agentic Pipeline Implementation: Develop modular agentic pipelines or basic multi-agent workflows for applications requiring LLM-based task orchestration.
- Voice Processing Pipeline Enhancement: Contribute to the development and improvement of voice processing systems including ASR and TTS.
- Performance Monitoring and Testing: Implement performance metrics, log monitoring, and validation workflows to ensure system reliability.
- Innovation and Research: Stay updated with evolving LLM tools, frameworks, and techniques. Propose and test innovations in the AI/ML space.
- Quality Assurance: Contribute to the development of tests, fail-safes, and robust deployment processes to maintain production quality.
Qualifications
- Bachelor’s degree in AI, Computer Science, Data Science, or related field. Master’s degree is a plus.
- 2–5 years of experience working on ML applications, preferably with NLP or LLM components.
- Proficient in Python and backend development frameworks (e.g., FastAPI, Flask).
- Hands-on experience with at least one LLM framework (Langchain, LlamaIndex, etc.).
- Familiarity with RAG pipelines, vector databases (e.g., Pinecone, FAISS), and API integration.
Soft Skills
- Strong problem-solving and analytical thinking.
- Effective communicator — able to explain technical challenges clearly.
- Independent and self-motivated, but also a strong team player.
- Open to feedback and continuous improvement.
Preferred Experience
- Experience deploying LLM applications to production or working with inference APIs.
- Exposure to microservices, containerization (Docker), and cloud platforms (AWS/GCP).
- Understanding of transformer models, embeddings, and tokenization.
Work Environment
Office-based environment with potential for hybrid or remote work depending on company policy.
Reporting
This position will report to the ML Lead.
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