Website:
swakio.com
Job details:
Experience: 5+ Years
Location: Remote – India
About the Role
We are looking for an experienced Senior AI/ML Engineer with 5+ years of hands-on experience in Machine Learning, Generative AI, LLMs, and production-grade AI systems.
The ideal candidate should have strong expertise in Python, ML/DL, LLM application development, RAG, AI agents, model deployment, and MLOps. You will be responsible for designing, developing, deploying, and optimizing scalable AI solutions that solve real-world business problems.
Key Responsibilities
- Design, develop, and deploy production-grade Machine Learning and Generative AI solutions.
- Build and optimize LLM-powered applications, including RAG systems, AI assistants, and intelligent automation workflows.
- Develop Agentic AI / AI agent systems with tool calling, workflow orchestration, memory, and multi-step reasoning.
- Build RAG pipelines involving document processing, chunking, embeddings, vector search, retrieval, reranking, and response generation.
- Work with LLMs from providers such as OpenAI, Anthropic, Google, Meta, or open-source/Hugging Face models.
- Develop and fine-tune ML/DL models using frameworks such as PyTorch, TensorFlow, or Scikit-learn.
- Design and implement ML pipelines, model serving, inference APIs, and scalable AI services.
- Implement model and LLM evaluation frameworks covering accuracy, relevance, hallucination, latency, cost, and safety.
- Build AI services and APIs using Python, FastAPI/Flask, and modern backend architectures.
- Deploy and operate AI/ML workloads using Docker, Kubernetes, CI/CD, and cloud platforms.
- Implement MLOps practices including model versioning, experiment tracking, monitoring, retraining, and production observability.
- Optimize model performance, inference latency, scalability, and cloud/GPU costs.
- Collaborate with product, engineering, data, and business teams to convert requirements into AI solutions.
- Mentor junior engineers and contribute to technical architecture and engineering best practices.
Required Skills
Core AI/ML
- 5+ years of professional experience in AI/ML, Machine Learning, Data Science, or related engineering roles.
- Strong proficiency in Python.
- Strong understanding of Machine Learning algorithms, statistics, feature engineering, model evaluation, and experimentation.
- Hands-on experience with Deep Learning using PyTorch and/or TensorFlow.
- Strong understanding of NLP and modern language-model architectures.
Generative AI / LLM
- Strong hands-on experience with LLMs and Generative AI applications.
- Experience building RAG-based applications.
- Strong understanding of:
- Embeddings
- Vector databases
- Semantic search
- Hybrid search
- Reranking
- Prompt engineering
- Context management
- LLM evaluation
- Experience with LangChain, LangGraph, LlamaIndex, or similar frameworks.
- Experience building AI agents, tool calling, multi-step workflows, or multi-agent systems is highly desirable.
- Experience with Hugging Face and/or open-source LLMs is a plus.
- Experience with LLM fine-tuning, LoRA/QLoRA, PEFT, or model adaptation is a plus.
MLOps & Cloud
- Hands-on experience deploying ML/AI applications to production.
- Experience with Docker and Kubernetes.
- Experience with MLflow, Kubeflow, or similar MLOps platforms.
- Experience with CI/CD pipelines and production monitoring.
- Experience with at least one major cloud platform: AWS, Azure, or GCP.
- Understanding of GPU-based inference and model optimization is a plus.
Data & Engineering
- Strong SQL and experience working with structured/unstructured data.
- Experience with REST APIs and microservices.
- Experience with databases such as PostgreSQL, MongoDB, or similar.
- Familiarity with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or FAISS.
- Strong Git and software engineering practices.
Good to Have
- Experience with AI evaluation and observability tools such as LangSmith, Ragas, TruLens, or equivalent.
- Experience with multimodal AI involving text, image, audio, or video.
- Experience with model quantization and inference optimization.
- Knowledge of AI safety, guardrails, responsible AI, and data privacy.
- Experience building AI products for SaaS, FinTech, Healthcare, or Enterprise applications.
- Open-source contributions, research publications, or strong GitHub projects.
Click on Apply to know more.
