Devsinc is looking to hire a highly skilled Software Engineer II – AI & Data Engineering with 2.5+ years of professional experience in building and deploying production-grade AI/ML systems, LLM-powered applications, and scalable data engineering solutions.
This role requires strong hands-on expertise in AI/ML Engineering, MLOps, Backend Engineering, and Data Engineering, with ownership across the complete lifecycle, from designing LLM applications, RAG pipelines, embeddings, and inference systems to building ETL/ELT pipelines, cloud-native infrastructure, and real-time data processing architectures.
Responsibilities:
- Design, develop, fine-tune, and deploy AI/ML models, including LLM-powered applications, RAG pipelines, embeddings, vector search architectures, and inference systems for real-world business use cases.
- Build and optimize high-performance Python-based APIs, microservices, and backend services for AI workloads, while collaborating with Engineering teams, Project Managers, and business stakeholders to deliver scalable, production-grade AI solutions.
- Design and implement MLOps workflows and cloud-native infrastructure across AWS, Azure, and GCP, including experiment tracking, model versioning, deployment automation, monitoring, and model optimization through hyperparameter tuning, quantization, and inference optimization.
- Design, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured datasets.
- Build and optimize data transformation, cleansing, validation, and quality frameworks, while working with distributed and streaming technologies such as Kafka, Spark, Kinesis, and Pub/Sub for real-time data processing.
- Ensure reliability, scalability, security, and cost optimization across AI and data infrastructure, while documenting architecture decisions, technical workflows, and engineering standards.
Requirements
- Bachelor’s degree in Computer Science, Software Engineering, AI, Data Science, or related field.
- 2.5+ years of hands-on experience in AI/ML Engineering, Data Engineering, or Backend Systems.
- Strong proficiency in Python and SQL, with hands-on experience in production-grade AI/data systems, relational/non-relational databases, and AI/ML libraries such as PyTorch, TensorFlow, Scikit-learn, Hugging Face, Pandas, and NumPy.
- Hands-on experience with data engineering frameworks such as Apache Spark, Airflow, dbt, or Databricks.
- Strong understanding of ML fundamentals, neural networks, NLP, model optimization, and hands-on experience with LLMs, RAG, embeddings, vector databases, and fine-tuning techniques (LoRA, PEFT, QLoRA).
- Proven experience in deploying AI models through APIs, microservices, and real-time inference systems, along with MLOps tools such as MLflow, SageMaker, Vertex AI, and Weights & Biases.
- Strong exposure to MLOps platforms and cloud ecosystems such as MLflow, SageMaker, Vertex AI, Weights & Biases, AWS, Azure, and GCP for model training, deployment, monitoring, and lifecycle management.
- Proficiency in Docker, Kubernetes, and CI/CD pipelines for containerization, orchestration, scalable deployments, and production environment management.
- Strong understanding of distributed systems, machine learning fundamentals, data architecture, security, and scalable system design.