Senior Software Engineer - AI, Data & Backend Systems
AI Summary
Devsinc is looking to hire a highly skilled Senior Software Engineer - Data+AI/ML with 4+ 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 build
About this role
Devsinc is looking to hire a highly skilled Senior Software Engineer - Data+AI/ML with 4+ 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, 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.
- 4+ years of hands-on experience in AI/ML Engineering, Data Science, 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.
