Senior Software Engineer

Description :

We are looking for an AI Software Developer to design, build, and maintain cloud-native backend services, infrastructure, and APIs that power AI features. This position will be full-time and Remote

What Youll Do :

- Develop infrastructure as code (IaC) using Terraform, CloudFormation, or ARM templates to automate cloud deployments.

- Implement CI/CD pipelines for AI model deployment, application updates, and automated testing (e.g., GitHub Actions, Azure DevOps).

- Build scalable APIs/microservices (FastAPI, gRPC) to serve AI features (e.g., LLM inference, agent workflows) with security, latency, and cost efficiency.

- Ensure reliability and observability via monitoring (Prometheus, CloudWatch), logging, and alerting for AI systems.

- AI Integration & Productionization (Secondary Focus) Integrate generative AI and agentic systems (e.g., LangChain, CrewAI, AutoGen) into full-stack applications-not just prototyping, but productionizing workflows.

- Design RAG pipelines with vector databases (e.g., Azure Cognitive Search, AWS OpenSearch) and optimize for latency/cost.

- Fine-tune LLMs (using LoRA, PEFT) or leverage cloud AI services (e.g., AWS Bedrock, Azure OpenAI) for custom use cases.

- Build data pipelines for AI training/inference (ingestion, preprocessing, synthetic data) with cloud tools (e.g., AWS Glue, Azure Data Factory).

- Collaborate with ML engineers to deploy models via TorchServe, Triton, or cloud-managed services (e.g., SageMaker Endpoints, Azure ML Endpoints).

- Collaboration & Ownership Work cross-functionally with product, frontend, and data teams to translate
business needs into scalable AI solutions.

- Champion software best practices : testing (unit/integration), code reviews, documentation, and modular design.

- Mentor junior engineers on cloud engineering and AI system design.

What You'll Need :

Required :

- Proficiency in Python (required) and modern frameworks (FastAPI, Flask, Django).

- Experience building cloud-native backend systems (AWS or Azure) with services like : Compute (EC2, Lambda, Azure Functions, VMs) Storage (S3, Blob Storage)

- Databases (RDS, Cosmos DB, DynamoDB) API gateways (API Gateway, Azure API Management)

- Hands-on experience with containerization (Docker) and orchestration (Kubernetes).

- Proven track record in CI/CD pipelines, infrastructure-as-code (Terraform/CloudFormation), and monitoring tools.

- 1-2 years of hands-on experience in AI application development, specifically :

- Building generative AI or agentic workflows (e.g., using LangChain, CrewAI, AutoGen).

- Implementing RAG pipelines or fine-tuning LLMs in production (e.g., via AWS Bedrock, Azure OpenAI, or open-source models).

- Experience with cloud AI services (SageMaker, Azure ML) or deploying open-source models on cloud infrastructure.

- Strong software engineering discipline

- Writing testable, maintainable code with unit/integration tests.