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Generative AI/ML Engineer
2 weeks ago
Job Description –
Role Summary
We are seeking a
Generative AI/ML Engineer
to design, build, and deploy intelligent AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, and edge computing. You will work hands-on with cloud-native AI services, LLMOps pipelines, and enterprise-grade deployment patterns to solve business problems.
Key Responsibilities
- Design, develop, and fine-tune LLM-powered applications for enterprise use cases.
- Experience in evaluating LLM applications and developing observability frameworks
- Implement
RAG pipelines
using vector databases, embeddings, and optimized retrieval strategies. - Build
agentic AI workflows
with multi-step reasoning, tool calling, and integration with APIs. - Integrate GenAI solutions into
multi-cloud or hybrid cloud environments
(AWS, Azure, GCP). - Develop and optimize
edge AI deployments
for low-latency use cases. - Create
data strategy, ingestion, transformation, enrichment, validations, quality checks via pipelines
for AI ingestion, preprocessing, and governance. - Implement
AI safety, bias mitigation, and compliance
measures. - Work closely with
LLMOps
teams to enable
continuous integration & deployment
of AI models. - Write well-documented, production-ready code in
Python, , Rust
. - Benchmark and evaluate
model performance
, latency, and cost-efficiency.
Required Skills
- Proficient in
cloud AI services
(e.g. AWS Bedrock/SageMaker, Azure AI Foundry, Google Vertex AI, Anthropic, OpenAI APIs). - Strong proficiency with
Python
and LLM frameworks (e.g. PromptFlow, LangGraph, LlamaIndex, HuggingFace, PyTorch, TensorFlow). - Hands-on experience with
vector DBs
(e.g. Pinecone, Weaviate, Milvus, FAISS, Azure Cognitive Search). - Experience building
RAG-based
and
agentic AI
solutions. - Familiarity with
Edge AI frameworks
(e.g. NVIDIA Jetson, AWS IoT Greengrass, Azure Percept). - Multi-modal AI (text, image, speech, video) experience.
- Strong grasp of
APIs, microservices, and event-driven architectures
. - Knowledge of
AI governance
(data privacy, model explainability, security). - Experience in
containerized deployments
(Docker, Kubernetes, serverless AI functions).
Preferred Skills
- Generative agents with
memory and planning capabilities
. - Real-time AI streaming with WebSockets or Kafka.
- Prior contributions to
open-source GenAI projects
. - Experience in build, test and deploy various ML models
- Experience in building MCP, A2A protocol