Gen ai developer

Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, Software Engineering, or equivalent.

4 to 7 years of hands-on experience in ML engineering, AI application development, or data science roles.

Minimum 2 years of demonstrated experience integrating AI/LLM/NLP capabilities in production environments.

Prior experience delivering cloud-native AI solutions on AWS.

AI/ML & Data Science Skills

Proficient in Python with ML frameworks such as scikit-learn, TensorFlow, PyTorch, or equivalent.

Strong understanding of supervised and unsupervised learning, time-series forecasting, classification, and clustering.

Hands-on experience with AWS SageMaker for model training, hosting, monitoring, and MLOps pipelines.

Familiarity with MLflow, Kubeflow, or equivalent MLOps tooling.

Proficient in feature engineering, data wrangling, and working with structured and unstructured data at scale.

LLM, GenAI & NLP Skills

Hands-on experience with LLM APIs such as OpenAI, AWS Bedrock, Anthropic, or Hugging Face.

Practical knowledge of RAG architecture, vector databases, and embedding pipelines.

Proficient in prompt engineering including zero-shot, few-shot, chain-of-thought, and tool-use patterns.

Experience building NLP pipelines including tokenization, NER, intent classification, summarization, and sentiment analysis.

Familiarity with agentic frameworks such as LangChain, CrewAI, AutoGen, or equivalent.

Software Engineering & AWS Integration

Proficient in Python and/or Java with experience exposing and consuming REST APIs in microservices architectures.

Hands-on experience with AWS services including Lambda, S3, SQS, SNS, CloudWatch, ECS, and IAM.

Familiarity with CI/CD pipelines using Jenkins, GitHub Actions, or GitLab CI.

Working knowledge of Docker, Kubernetes, or serverless deployment patterns for AI workloads.

Proficient in SQL; experience with data lakes, streaming data, or event-driven architectures is an added advantage.