Now Hiring: Memory Circuit Design Verification Engineer / Senior Engineer Location : Hyderabad, India Experience : 7+ Years Start Date : Immediate or within 15 days Are you an expert in memory circuit verification with a passion for cutting-edge technology? Join us in pushing the boundaries of DRAM and emerging memory design as a Senior Verification Engineer ! Key Responsibilities Perform pre-silicon verification of custom gate-level designs across memory technologies (DDR4/5, LPDDR4/5) Simulate, debug, and validate full-chip and block-level designs Develop and maintain test benches, functional vectors, and regressions Write functional test cases and analyze coverage reports Collaborate with global teams on design and verification Drive improvements in verification methodologies Must-Have Skills Strong foundation in CMOS fundamentals and circuit-level design Proficiency in Verilog and SPICE simulations Hands-on experience in writing and executing regressions Exceptional debugging and analytical skills Familiarity with memory protocols : DDR4/5, LPDDR4/5 Preferred Skills Experience with System Verilog , UVM , and PLI Scripting with Python, Perl Exposure to AMS verification / mixed-signal co-simulation Background in DRAM, SRAM , or related memory technologies Qualification B.E./B.Tech or M.E./M.Tech in Electronics, Electrical, VLSI , or a related discipline Why Join Us? Be part of a dynamic team that is at the forefront of memory innovation . This is your chance to contribute to industry-leading technologies and take ownership of complex verification challenges. Apply now or refer a candidate! DM us or send your resume to vignesh@mileveen.com Show more Show less
Job Title: Demand Analyst / NPF Analyst Payroll: Xeedo Client: Micron Location: Acquila Building, Financial District, Hyderabad Job Code: MO31JP00005233 Industry: Semiconductor Work Mode: Onsite – Monday to Friday (Full-time from Office) Job Summary: We are seeking a detail-oriented Demand Analyst / NPF Analyst with a solid foundation in the semiconductor domain . The ideal candidate will take responsibility for managing and optimizing the Non-Plannable Flag (NPF) process while ensuring the accurate application of Re-Order Point (ROP) logic . Proficiency in SAP Rapid Response is crucial for effective demand planning and to ensure operational excellence. Interview Process: Level 1: Virtual Interview Level 2: Face-to-Face Interview Key Responsibilities: Manage the NPF process and ensure the ROP logic is applied consistently across systems. Perform regular data refreshes and validate alignment across various platforms. Analyze flagged orders, identify root causes, and drive resolution by collaborating with relevant teams. Work closely with Sales, Business Units, Order Management , and IT to ensure process consistency and resolve issues effectively. Proactively prioritize turnaround time by reviewing and resolving ROP-gated orders . Participate in process reviews and contribute to the enhancement of systems and reporting tools. Drive seamless daily operations , ensuring data integrity and compliance with processes. Utilize SAP Rapid Response for end-to-end demand planning and supply chain optimization. Required Skills & Experience: Industry Experience: Minimum 2 years in the Semiconductor industry preferred. Demand Planning Tools: Proficiency in SAP Rapid Response is mandatory. Analytical Thinking: Strong ability to analyze flagged orders, identify issues, and provide timely solutions. Agility & Proactiveness: Quick decision-making ability and efficient handling of escalations. Cross-Functional Collaboration: Experience working with Sales , Business Units , Order Management , and IT teams. Communication Skills: Strong verbal and written communication to effectively convey issues and align teams for resolution. Interested candidate send the updated resume to geethapriya.s@mileveen.com
As a Python Developer at Poonawalla Fincorp, you will be responsible for developing and maintaining Python applications using FastAPI, Flask, and libraries like NumPy and Torch. You will design schemas using FieldSchema and CollectionSchema in Milvus, integrate Milvus with Python applications for high-performance vector search, and utilize PyMilvus to manage collections, indexes, and perform vector similarity searches. Your role will also involve implementing embedding models such as Deepface and Arcface to convert raw data into vector representations, building and optimizing semantic search, recommendation systems, and image similarity pipelines, and creating and managing vector indexes while applying metadata filtering using scalar fields. Additionally, you will document data schemas, workflows, and APIs with clarity and collaborate with teams to deploy solutions using Docker, Kubernetes, or cloud platforms such as AWS, GCP, or Azure. Your knowledge of Approximate Nearest Neighbor (ANN) algorithms and vector indexing strategies will be crucial in improving search efficiency. Key Responsibilities: - Develop and maintain Python applications using FastAPI, Flask, and libraries like NumPy and Torch. - Design schemas using FieldSchema and CollectionSchema in Milvus. - Integrate Milvus with Python applications for high-performance vector search. - Utilize PyMilvus to manage collections, indexes, and perform vector similarity searches. - Implement embedding models (e.g., Deepface, Arcface) to convert raw data into vector representations. - Build and optimize semantic search, recommendation systems, and image similarity pipelines. - Create and manage vector indexes and apply metadata filtering using scalar fields. - Document data schemas, workflows, and APIs with clarity. - Collaborate with teams to deploy solutions using Docker, Kubernetes, or cloud platforms (AWS, GCP, Azure). - Apply knowledge of Approximate Nearest Neighbor (ANN) algorithms and vector indexing strategies to improve search efficiency. Qualifications Required: - 3-4 years of Python development experience. - Strong proficiency in Python, with experience in FastAPI/Flask, NumPy, and Torch. - Hands-on experience with Milvus DB, PyMilvus, and vector database concepts. - Solid understanding of embedding models and vector-based search techniques. - Familiarity with semantic search, recommendation engines, and image similarity pipelines. - Experience with Docker, Kubernetes, or cloud deployment (AWS, GCP, Azure). - Knowledge of ANN algorithms and indexing strategies. - Excellent documentation and communication skills.,