Devsinc is hiring a Senior AI/ML Engineer with 4+ years of experience, including development of LLM or generative AI applications. The ideal candidate brings strong machine learning fundamentals and expertise in Python backend services and APIs, RAG, semantic search, AI agents, and scalable ML infrastructure, using commercial and open-source models.

You will own the end-to-end AI lifecycle, from experimentation and evaluation to deployment, optimization, and monitoring, ensuring reliable, scalable, secure, and cost-efficient solutions. The role also involves architectural decisions, mentoring engineers, and collaborating with clients and cross-functional teams to deliver measurable business impact.

Responsibilities

• Design, develop, and deploy AI/ML and LLM-based applications, including AI agents, tool-using systems, and human-in-the-loop workflows, to solve business problems.
• Build scalable training, fine-tuning, evaluation, and inference pipelines, with experiment tracking and model versioning.
• Develop and optimize RAG and semantic-search systems using embeddings, document chunking, vector search, reranking, and grounding.
• Build backend APIs, microservices, and real-time inference services using Python, FastAPI, Flask, or Django.
• Improve model quality, latency, throughput, and cost through experimentation, hyperparameter tuning, quantization, batching, and caching.
• Implement MLOps, automated testing, CI/CD, and production monitoring, supported by evaluation datasets, quality criteria, regression tests, and human review.
• Guide architectural decisions and cloud deployment, ensuring scalability, reliability, security, and resource efficiency, with safeguards against prompt injection, data leakage, unauthorized access, and unsafe outputs.
• Evaluate emerging AI technologies and measure feature effectiveness through product analytics or A/B testing.
• Mentor engineers, collaborate with technical and non-technical stakeholders, and document designs, experiments, and outcomes.

Requirements

• Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related field.
• 4+ years of post-graduation professional experience in AI/ML engineering, with demonstrated ownership of production AI systems and hands-on experience developing LLM or generative AI applications.
• Strong production-level Python skills, with hands-on experience in PyTorch and/or TensorFlow and solid knowledge of machine learning, neural networks, NLP, feature engineering, and model optimization.
• Experience integrating commercial or open-source LLMs, including prompt design, structured outputs, tool calling, context management, and model limitations.
• Hands-on experience building RAG or semantic-search systems, including embeddings, chunking, retrieval, reranking, and grounding, using vector-search solutions such as pgvector, Pinecone, Weaviate, Qdrant, Milvus, or Elasticsearch.
• Experience developing and deploying APIs, microservices, or inference services using FastAPI, Flask, Django, or equivalent frameworks, with proficiency in SQL and PostgreSQL or MySQL.
• Experience deploying AI solutions on AWS, Azure, or Google Cloud, with working knowledge of Git, Docker, automated testing, CI/CD, and MLOps, including experiment tracking, model versioning, and monitoring.
• Understanding of AI evaluation, regression testing, human review, and security and privacy risks.
• Ability to own technical decisions, guide engineers, and communicate effectively with clients and cross-functional stakeholders.

Preferred Skills & Experience

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