Senior Software Engineer - AI/ML

Devsinc is seeking a Senior AI/ML Engineer with over 4 years of professional experience, particularly in developing large language models (LLM) or generative AI applications. The ideal candidate will have a solid foundation in machine learning principles and strong expertise in building Python backend services and APIs. Experience with Retrieval-Augmented Generation (RAG), semantic search, AI agents, and scalable machine learning infrastructure using both commercial and open-source models is essential. This role involves full ownership of the AI lifecycle, from experimentation and evaluation to deployment, optimization, and ongoing monitoring, ensuring solutions are reliable, scalable, secure, and cost-effective. The engineer will also contribute to architectural decisions, mentor junior engineers, and collaborate closely with clients and cross-functional teams to drive measurable business outcomes.

Key Responsibilities

- Design, develop, and deploy AI/ML and LLM-based applications, including AI agents, tool-using systems, and human-in-the-loop workflows to address complex business challenges.
- Build scalable pipelines for training, fine-tuning, evaluation, and inference, incorporating experiment tracking and model versioning.
- Develop and optimize RAG and semantic search systems using embeddings, document chunking, vector search, reranking, and grounding techniques.
- Create backend APIs, microservices, and real-time inference services using Python frameworks such as FastAPI, Flask, or Django.
- Enhance model performance by improving quality, latency, throughput, and cost through experimentation, hyperparameter tuning, quantization, batching, and caching.
- Implement MLOps best practices, including automated testing, continuous integration/continuous deployment (CI/CD), and production monitoring supported by evaluation datasets, quality criteria, regression tests, and human review.
- Lead architectural decisions and cloud deployments, ensuring scalability, reliability, security, and resource efficiency while safeguarding against prompt injection, data leakage, unauthorized access, and unsafe outputs.
- Evaluate emerging AI technologies and assess feature effectiveness through product analytics or A/B testing.
- Mentor engineering teams, collaborate with technical and non-technical stakeholders, and document designs, experiments, and results thoroughly.

Required Qualifications

- Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related field.
- Minimum of 4 years of post-graduation experience in AI/ML engineering, with proven ownership of production AI systems and hands-on development of LLM or generative AI applications.
- Strong production-level Python programming skills, with practical experience in PyTorch and/or TensorFlow.
- Solid understanding of machine learning concepts, neural networks, natural language processing (NLP), feature engineering, and model optimization.
- Experience integrating commercial or open-source LLMs, including prompt design, structured outputs, tool calling, context management, and managing model limitations.
- Hands-on expertise in building RAG or semantic search systems using vector search technologies such as pgvector, Pinecone, Weaviate, Qdrant, Milvus, or Elasticsearch.
- Proficiency in developing and deploying APIs, microservices, or inference services with frameworks like FastAPI, Flask, or Django, along with strong SQL skills and experience with PostgreSQL or MySQL.
- Experience deploying AI solutions on cloud platforms such as AWS, Azure, or Google Cloud.
- Familiarity with Git, Docker, automated testing, CI/CD pipelines, and MLOps practices including experiment tracking, model versioning, and monitoring.
- Understanding of AI evaluation methodologies, regression testing, human review processes, and security and privacy risks.
- Strong leadership skills with the ability to make technical decisions, guide engineers, and communicate effectively with clients and cross-functional teams.

Preferred Qualifications and Experience

- Experience with AI frameworks such as LangChain, LlamaIndex, or LangGraph, and evaluation tools like LangSmith, Langfuse, or Ragas.
- Familiarity with Hugging Face Transformers, vLLM, Ollama, LoRA/PEFT fine-tuning, or self-hosted models.
- Knowledge of MLflow, Kubeflow, Kubernetes, Terraform, distributed systems, or GPU acceleration.
- Experience with data orchestration, asynchronous processing, caching, or messaging systems using tools such as Airflow, Redis, Celery, Kafka, or RabbitMQ.
- Understanding of advanced retrieval techniques, knowledge graphs, recommendation systems, computer vision, multimodal AI, or A/B testing and product analytics.

This position offers the opportunity to work on cutting-edge AI technologies in a collaborative environment, driving impactful solutions that scale across industries.